Style transfer aims to combine the content of one image with the artistic style of another. It was discovered that lower levels of convolutional networks captured style information, while higher levels captures content information. The original style transfer formulation used a weighted combination of VGG-16 layer activations to achieve this goal. Later, this was accomplished in real-time using a feed-forward network to learn the optimal combination of style and content features from the respective images. The first aim of our project was to introduce a framework for capturing the style from several images at once. We propose a method that extends the original real-time style transfer formulation by combining the features of several style images. This method successfully captures color information from the separate style images. The other aim of our project was to improve the temporal style continuity from frame to frame. Accordingly, we have experimented with the temporal stability of the output images and discussed the various available techniques that could be employed as alternatives.
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
Hybrid compression based stationary wavelet transformsOmar Ghazi
This document presents a hybrid compression approach for images that uses Stationary Wavelet Transforms (SWT), Back Propagation Neural Network (BPNN), and Lempel-Ziv-Welch (LZW) compression. The approach involves: 1) preprocessing the image, 2) applying SWT, 3) converting to a 1D vector using zigzag scan, and 4) hybrid compression using BPNN vector quantization and LZW lossless compression. Experimental results show the SWT with BPNN and LZW achieves the highest compression ratios but the longest processing time, while SWT with Run Length encoding has a lower ratio but shorter time. The hybrid approach combines lossy and lossless compression techniques to obtain a
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
1) The document presents a method for image compression that combines linear vector quantization and discrete wavelet transform.
2) Linear vector quantization is used to generate codebooks and encode image blocks, achieving better PSNR and MSE than self-organizing maps.
3) The encoded blocks are then subjected to discrete wavelet transform. Low-low subbands are stored for reconstruction while other subbands are discarded.
4) Experimental results show the proposed method achieves higher PSNR and lower MSE than existing techniques, preserving both texture and edge information.
We presents a deep architecture for dense semantic correspondence, called pyramidal affine regression networks (PARN), that estimates locally-varying affine transformation fields across images.
To deal with intra-class appearance and shape variations that commonly exist among different instances within the same object category,
we leverage a pyramidal model where affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed within deep networks.
PARN estimates residual affine transformations at each level and composes them to estimate final affine transformations.
Furthermore, to overcome the limitations of insufficient training data for semantic correspondence, we propose a novel weakly-supervised training scheme that generates progressive supervisions by leveraging a correspondence consistency across image pairs.
Our method is fully learnable in an end-to-end manner and does not require quantizing infinite continuous affine transformation fields.
A novel secure image steganography method based on chaos theory in spatial do...ijsptm
This paper presents a novel approach of building a secure data hiding technique in digital images. The
image steganography technique takes the advantage of limited power of human visual system (HVS). It uses
image as cover media for embedding secret message. The most important requirement for a steganographic
algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is
proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover
image. A 3-3-2 LSB insertion method has been used for image steganography. Experimental results show a
substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the
proposed technique over the base technique of 3-3-2 LSB insertion.
This project report summarizes research on using deep learning methods for super resolution of document images to improve optical character recognition (OCR) accuracy. Two approaches are studied: SRCNN and incorporating OCR accuracy into the loss function. Experiments include modifying the dataset to focus on text regions and varying the SRCNN architecture. Results show the 2-layer SRCNN trained on the modified dataset achieves the best OCR accuracy, exceeding the ground truth in some cases.
HRNET : Deep High-Resolution Representation Learning for Human Pose Estimationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다! 오늘 소개 드릴 논문은 Deep High-Resolution Representation Learning for Human Pose Estimation 라는 제목의 논문입니다.
오늘 소개드릴 논문은 Pose Estimation에 관련된 논문 입니다. 기존 Pose Estimation 모델의 경우 직렬적인 네트워크 구조를 지녔지만, 직렬적인 구조는 압축하는 과정에서
지엽적인 정보들의 손실을 가져오게 되고 모든 프로세스가 upsampling에 과도하게 의존하고 있다는 한계점을 가지고 있습니다.그래서 이러한 한계점을 극복하고자HRNet은 이러한 직렬 구조에서 벗어나 병렬 구조로 subnetwork를 구성했습니다.
This document discusses parallelizing graph algorithms on GPUs for optimization. It summarizes previous work on parallel Breadth-First Search (BFS), All Pair Shortest Path (APSP), and Traveling Salesman Problem (TSP) algorithms. It then proposes implementing BFS, APSP, and TSP on GPUs using optimization techniques like reducing data transfers between CPU and GPU and modifying the algorithms to maximize GPU computing power and memory usage. The paper claims this will improve performance and speedup over CPU implementations. It focuses on optimizing graph algorithms for parallel GPU processing to accelerate applications involving large graph analysis and optimization problems.
Multi Wavelet for Image Retrival Based On Using Texture and Color QuerysIOSR Journals
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
Hybrid compression based stationary wavelet transformsOmar Ghazi
This document presents a hybrid compression approach for images that uses Stationary Wavelet Transforms (SWT), Back Propagation Neural Network (BPNN), and Lempel-Ziv-Welch (LZW) compression. The approach involves: 1) preprocessing the image, 2) applying SWT, 3) converting to a 1D vector using zigzag scan, and 4) hybrid compression using BPNN vector quantization and LZW lossless compression. Experimental results show the SWT with BPNN and LZW achieves the highest compression ratios but the longest processing time, while SWT with Run Length encoding has a lower ratio but shorter time. The hybrid approach combines lossy and lossless compression techniques to obtain a
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
1) The document presents a method for image compression that combines linear vector quantization and discrete wavelet transform.
2) Linear vector quantization is used to generate codebooks and encode image blocks, achieving better PSNR and MSE than self-organizing maps.
3) The encoded blocks are then subjected to discrete wavelet transform. Low-low subbands are stored for reconstruction while other subbands are discarded.
4) Experimental results show the proposed method achieves higher PSNR and lower MSE than existing techniques, preserving both texture and edge information.
We presents a deep architecture for dense semantic correspondence, called pyramidal affine regression networks (PARN), that estimates locally-varying affine transformation fields across images.
To deal with intra-class appearance and shape variations that commonly exist among different instances within the same object category,
we leverage a pyramidal model where affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed within deep networks.
PARN estimates residual affine transformations at each level and composes them to estimate final affine transformations.
Furthermore, to overcome the limitations of insufficient training data for semantic correspondence, we propose a novel weakly-supervised training scheme that generates progressive supervisions by leveraging a correspondence consistency across image pairs.
Our method is fully learnable in an end-to-end manner and does not require quantizing infinite continuous affine transformation fields.
A novel secure image steganography method based on chaos theory in spatial do...ijsptm
This paper presents a novel approach of building a secure data hiding technique in digital images. The
image steganography technique takes the advantage of limited power of human visual system (HVS). It uses
image as cover media for embedding secret message. The most important requirement for a steganographic
algorithm is to be imperceptible while maximizing the size of the payload. In this paper a method is
proposed to encrypt the secret bits of the message based on chaos theory before embedding into the cover
image. A 3-3-2 LSB insertion method has been used for image steganography. Experimental results show a
substantial improvement in the Peak Signal to Noise Ratio (PSNR) and Image Fidelity (IF) value of the
proposed technique over the base technique of 3-3-2 LSB insertion.
This project report summarizes research on using deep learning methods for super resolution of document images to improve optical character recognition (OCR) accuracy. Two approaches are studied: SRCNN and incorporating OCR accuracy into the loss function. Experiments include modifying the dataset to focus on text regions and varying the SRCNN architecture. Results show the 2-layer SRCNN trained on the modified dataset achieves the best OCR accuracy, exceeding the ground truth in some cases.
HRNET : Deep High-Resolution Representation Learning for Human Pose Estimationtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다! 오늘 소개 드릴 논문은 Deep High-Resolution Representation Learning for Human Pose Estimation 라는 제목의 논문입니다.
오늘 소개드릴 논문은 Pose Estimation에 관련된 논문 입니다. 기존 Pose Estimation 모델의 경우 직렬적인 네트워크 구조를 지녔지만, 직렬적인 구조는 압축하는 과정에서
지엽적인 정보들의 손실을 가져오게 되고 모든 프로세스가 upsampling에 과도하게 의존하고 있다는 한계점을 가지고 있습니다.그래서 이러한 한계점을 극복하고자HRNet은 이러한 직렬 구조에서 벗어나 병렬 구조로 subnetwork를 구성했습니다.
This document discusses parallelizing graph algorithms on GPUs for optimization. It summarizes previous work on parallel Breadth-First Search (BFS), All Pair Shortest Path (APSP), and Traveling Salesman Problem (TSP) algorithms. It then proposes implementing BFS, APSP, and TSP on GPUs using optimization techniques like reducing data transfers between CPU and GPU and modifying the algorithms to maximize GPU computing power and memory usage. The paper claims this will improve performance and speedup over CPU implementations. It focuses on optimizing graph algorithms for parallel GPU processing to accelerate applications involving large graph analysis and optimization problems.
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis taeseon ryu
해당 논문은 3D Aware 모델입니다 StyleGAN 같은 경우에는 어떤 하나의 피처에 대해서 Editing 하고 싶을 때 입력에 해당하는 레이턴트 백터를 찾아서 레이턴트 백터를 수정함으로써 입에 해당하는 피쳐를 바꿀 수 있었는데 이런 컨셉을 그대로 착안해서
GAN 스페이스 논문에서는 인풋이 들어왔을 때 어떤 공간적인 정보까지도 에디팅하려고 시도했습니다 결과를 봤을 때 로테이션 정보가 어느 정도 잘 학습된 것 같지만 같은 사람이 아닌 것 같이 인식되기도 합니다 이러한 문제를 이제 disentangle 되지 않았다라고 하는 게 원하는 피처만 변화시켜야 되는 것과 달리 다른 피처까지도 모두 학습 모두 변했다는 것인데 이를 좀 더 효율적으로 3D를 더 잘 이해시키기 위해서 탄생한 논문입니다.
Semantic Image Retrieval Using Relevance Feedback dannyijwest
This paper presents optimized interactive content-based image retrieval framework based on AdaBoost
learning method. As we know relevance feedback (RF) is online process, so we have optimized the learning
process by considering the most positive image selection on each feedback iteration. To learn the system we
have used AdaBoost. The main significances of our system are to address the small training sample and to
reduce retrieval time. Experiments are conducted on 1000 semantic colour images from Corel database to
demonstrate the effectiveness of the proposed framework. These experiments employed large image
database and combined RCWFs and DT-CWT texture descriptors to represent content of the images.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
MetaPerturb is a meta-learned perturbation function that can enhance generalization of neural networks on different tasks and architectures. It proposes a novel meta-learning framework involving jointly training a main model and perturbation module on multiple source tasks to learn a transferable perturbation function. This meta-learned perturbation function can then be transferred to improve performance of a target model on an unseen target task or architecture, outperforming baselines on various datasets and architectures.
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
1. The document discusses the history and development of convolutional neural networks (CNNs) for computer vision tasks like image classification.
2. Early CNN models from 2012 included AlexNet which achieved breakthrough results on ImageNet classification. Later models improved performance through increased depth like VGGNet in 2014.
3. Recent models like ResNet in 2015 and DenseNet in 2016 addressed the degradation problem of deeper networks through shortcut connections, achieving even better results on image classification tasks. New regularization techniques like Dropout, Batch Normalization, and DropBlock have helped training of deeper CNNs.
This document proposes an adaptive steganography technique for hiding secret data in digital images. The technique uses variable bit length embedding based on wavelet coefficients of the cover image. A logistic map is used to generate a secret key, which determines the RGB color planes and blocks used for data hiding. Wavelet coefficients are classified into ranges, and the number of bits hidden corresponds to the coefficient value range. Extraction involves selecting the same coefficients based on the key and calculating the hidden bits. The technique aims to improve security, capacity and imperceptibility compared to existing constant bit length methods. Evaluation shows the proposed method provides good security since variable bits are hidden randomly using the secret key.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Performance analysis of Hybrid Transform, Hybrid Wavelet and Multi-Resolutio...IJMER
This document discusses and compares different methods for image data compression, including hybrid transform, hybrid wavelet transform, and multi-resolution hybrid wavelet transform. It first provides background on image compression techniques and reviews related work. It then proposes using Kekre transform and Hartley transform to generate a hybrid wavelet transform for image compression. The performance of this hybrid wavelet transform is analyzed and compared to a hybrid transform and multi-resolution hybrid wavelet transform in terms of root mean square error, mean absolute error, and average fractional change in pixel value across different compression ratios using various test images. The hybrid wavelet transform is found to provide lower error values than the other two methods.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The document compares the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for training a multilayer perceptron neural network for image compression. It finds that while both algorithms performed comparably in terms of accuracy and speed, the Levenberg-Marquardt algorithm achieved slightly better accuracy as measured by average training accuracy and mean squared error, while the Scaled Conjugate Gradient algorithm was faster as measured by average training iterations. The document compresses a standard test image called Lena using both algorithms and analyzes the results.
Modern Convolutional Neural Network techniques for image segmentationGioele Ciaparrone
Recently, Convolutional Neural Networks have been successfully applied to image segmentation tasks. Here we present some of the most recent techniques that increased the accuracy in such tasks. First we describe the Inception architecture and its evolution, which allowed to increase width and depth of the network without increasing the computational burden. We then show how to adapt classification networks into fully convolutional networks, able to perform pixel-wise classification for segmentation tasks. We finally introduce the hypercolumn technique to further improve state-of-the-art on various fine-grained localization tasks.
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...IOSR Journals
This document presents a method for using a multi-layered feed-forward neural network (MLFNN) architecture as a bidirectional associative memory (BAM) for function approximation. It proposes applying the backpropagation algorithm in two phases - first in the forward direction, then in the backward direction - which allows the MLFNN to work like a BAM. Simulation results show that this two-phase backpropagation algorithm achieves convergence faster than standard backpropagation when approximating the sine function, demonstrating that the MLFNN architecture is better suited for function approximation when trained this way.
Sequential Reptile_Inter-Task Gradient Alignment for Multilingual LearningMLAI2
Multilingual models jointly pretrained on multiple languages have achieved remarkable performance on various multilingual downstream tasks. Moreover, models finetuned on a single monolingual downstream task have shown to generalize to unseen languages. In this paper, we first show that it is crucial for those tasks to align gradients between them in order to maximize knowledge transfer while minimizing negative transfer. Despite its importance, the existing methods for gradient alignment either have a completely different purpose, ignore inter-task alignment, or aim to solve continual learning problems in rather inefficient ways. As a result of the misaligned gradients between tasks, the model suffers from severe negative transfer in the form of catastrophic forgetting of the knowledge acquired from the pretraining. To overcome the limitations, we propose a simple yet effective method that can efficiently align gradients between tasks. Specifically, we perform each inner-optimization by sequentially sampling batches from all the tasks, followed by a Reptile outer update. Thanks to the gradients aligned between tasks by our method, the model becomes less vulnerable to negative transfer and catastrophic forgetting. We extensively validate our method on various multi-task learning and zero-shot cross-lingual transfer tasks, where our method largely outperforms all the relevant baselines we consider.
A beginner's guide to Style Transfer and recent trendsJaeJun Yoo
Style transfer techniques have evolved from matching gram matrices to using neural networks. Early methods matched gram statistics of CNN features to transfer texture styles. Recent work uses adaptive instance normalization and feed-forward networks. WCT2 achieves photorealistic transfer using wavelet transforms that satisfy the perfect reconstruction condition, enabling high resolution stylization and temporal consistency in videos without post-processing.
Explores the type of structure learned by Convolutional Neural Networks, the applications where they're most valuable and a number of appropriate mental models for understanding deep learning.
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
One of the important steps in routing is to find a feasible path based on the state information. In order to support real-time multimedia applications, the feasible path that satisfies one or more constraints has to be computed within a very short time. Therefore, the paper presents a genetic algorithm to solve the paths tree problem subject to cost constraints. The objective of the algorithm is to find the set of edges connecting all nodes such that the sum of the edge costs from the source (root) to each node is minimized. I.e. the path from the root to each node must be a minimum cost path connecting them. The algorithm has been applied on two sample networks, the first network with eight nodes, and the last one with eleven nodes to illustrate its efficiency.
1. The document describes PinSage, a graph convolutional neural network model for recommender systems applied at scale to Pinterest.
2. PinSage models the Pinterest environment as a bipartite graph and uses localized graph convolutions with importance-based sampling to generate embeddings of pins for recommendation.
3. It trains the model with a max-margin loss function on large minibatches processed in parallel using multiple GPUs and achieves state-of-the-art performance on pin recommendation.
In this project, we consider the deep learning-based approaches to performing Neural Style Transfer (NST) on images. In particular, we intend to assess the Real-Time performance of this approach, since it has become a trending topic both in academia and in industrial applications.
For this purpose, after exploring the perceptual loss concept, which is used by the majority of models when performing NST, we conducted a review on a range of existing methods for this practical problem. We found that the feedforward based methods allow to achieve real time performance as opposed to the framework of iterative optimization proposed in the original Neural Style Transfer algorithm introduced by Gatys et al. Which is why we mainly focused on two feed-forward methods proposed in the literature: one that focuses on Single-Style transfer, TransformNet, and one that tackles the more generic problem of Multiple Style Transfer, MSG-Net.
This document summarizes a student project report on neural style transfer. The students tested existing feed-forward neural style transfer methods (TransformNet and MSG-Net) and compared their real-time performance and visual quality. They also experimented with combining multiple styles using the original optimization-based neural style transfer algorithm. The document provides background on neural style transfer techniques, discusses the methods tested, and outlines the project's methodology for evaluating and comparing the methods.
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis taeseon ryu
해당 논문은 3D Aware 모델입니다 StyleGAN 같은 경우에는 어떤 하나의 피처에 대해서 Editing 하고 싶을 때 입력에 해당하는 레이턴트 백터를 찾아서 레이턴트 백터를 수정함으로써 입에 해당하는 피쳐를 바꿀 수 있었는데 이런 컨셉을 그대로 착안해서
GAN 스페이스 논문에서는 인풋이 들어왔을 때 어떤 공간적인 정보까지도 에디팅하려고 시도했습니다 결과를 봤을 때 로테이션 정보가 어느 정도 잘 학습된 것 같지만 같은 사람이 아닌 것 같이 인식되기도 합니다 이러한 문제를 이제 disentangle 되지 않았다라고 하는 게 원하는 피처만 변화시켜야 되는 것과 달리 다른 피처까지도 모두 학습 모두 변했다는 것인데 이를 좀 더 효율적으로 3D를 더 잘 이해시키기 위해서 탄생한 논문입니다.
Semantic Image Retrieval Using Relevance Feedback dannyijwest
This paper presents optimized interactive content-based image retrieval framework based on AdaBoost
learning method. As we know relevance feedback (RF) is online process, so we have optimized the learning
process by considering the most positive image selection on each feedback iteration. To learn the system we
have used AdaBoost. The main significances of our system are to address the small training sample and to
reduce retrieval time. Experiments are conducted on 1000 semantic colour images from Corel database to
demonstrate the effectiveness of the proposed framework. These experiments employed large image
database and combined RCWFs and DT-CWT texture descriptors to represent content of the images.
Content Based Image Retrieval Using 2-D Discrete Wavelet TransformIOSR Journals
This document proposes a content-based image retrieval system using 2D discrete wavelet transform and texture features. The system decomposes images using 2D DWT, extracts texture features from low frequency coefficients using GLCM, and retrieves similar images by calculating Euclidean distances between feature vectors. Experimental results on Wang's database show the proposed approach achieves 89.8% average retrieval accuracy.
MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and ArchitecturesMLAI2
MetaPerturb is a meta-learned perturbation function that can enhance generalization of neural networks on different tasks and architectures. It proposes a novel meta-learning framework involving jointly training a main model and perturbation module on multiple source tasks to learn a transferable perturbation function. This meta-learned perturbation function can then be transferred to improve performance of a target model on an unseen target task or architecture, outperforming baselines on various datasets and architectures.
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
1. The document discusses the history and development of convolutional neural networks (CNNs) for computer vision tasks like image classification.
2. Early CNN models from 2012 included AlexNet which achieved breakthrough results on ImageNet classification. Later models improved performance through increased depth like VGGNet in 2014.
3. Recent models like ResNet in 2015 and DenseNet in 2016 addressed the degradation problem of deeper networks through shortcut connections, achieving even better results on image classification tasks. New regularization techniques like Dropout, Batch Normalization, and DropBlock have helped training of deeper CNNs.
This document proposes an adaptive steganography technique for hiding secret data in digital images. The technique uses variable bit length embedding based on wavelet coefficients of the cover image. A logistic map is used to generate a secret key, which determines the RGB color planes and blocks used for data hiding. Wavelet coefficients are classified into ranges, and the number of bits hidden corresponds to the coefficient value range. Extraction involves selecting the same coefficients based on the key and calculating the hidden bits. The technique aims to improve security, capacity and imperceptibility compared to existing constant bit length methods. Evaluation shows the proposed method provides good security since variable bits are hidden randomly using the secret key.
Convolutional neural network from VGG to DenseNetSungminYou
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
CONTENT BASED VIDEO CATEGORIZATION USING RELATIONAL CLUSTERING WITH LOCAL SCA...ijcsit
This paper introduces a novel approach for efficient video categorization. It relies on two main
components. The first one is a new relational clustering technique that identifies video key frames by
learning cluster dependent Gaussian kernels. The proposed algorithm, called clustering and Local Scale
Learning algorithm (LSL) learns the underlying cluster dependent dissimilarity measure while finding
compact clusters in the given dataset. The learned measure is a Gaussian dissimilarity function defined
with respect to each cluster. We minimize one objective function to optimize the optimal partition and the
cluster dependent parameter. This optimization is done iteratively by dynamically updating the partition
and the local measure. The kernel learning task exploits the unlabeled data and reciprocally, the
categorization task takes advantages of the local learned kernel. The second component of the proposed
video categorization system consists in discovering the video categories in an unsupervised manner using
the proposed LSL. We illustrate the clustering performance of LSL on synthetic 2D datasets and on high
dimensional real data. Also, we assess the proposed video categorization system using a real video
collection and LSL algorithm.
Performance analysis of Hybrid Transform, Hybrid Wavelet and Multi-Resolutio...IJMER
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Learning a Recurrent Visual Representation for Image Caption GJospehStull43
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Learning a Recurrent Visual Representation for Image Caption G.docxcroysierkathey
Learning a Recurrent Visual Representation for Image Caption Generation
Xinlei Chen
Carnegie Mellon University
[email protected]
C. Lawrence Zitnick
Microsoft Research, Redmond
[email protected]
Abstract
In this paper we explore the bi-directional mapping be-
tween images and their sentence-based descriptions. We
propose learning this mapping using a recurrent neural net-
work. Unlike previous approaches that map both sentences
and images to a common embedding, we enable the gener-
ation of novel sentences given an image. Using the same
model, we can also reconstruct the visual features associ-
ated with an image given its visual description. We use a
novel recurrent visual memory that automatically learns to
remember long-term visual concepts to aid in both sentence
generation and visual feature reconstruction. We evaluate
our approach on several tasks. These include sentence gen-
eration, sentence retrieval and image retrieval. State-of-
the-art results are shown for the task of generating novel im-
age descriptions. When compared to human generated cap-
tions, our automatically generated captions are preferred
by humans over 19.8% of the time. Results are better than
or comparable to state-of-the-art results on the image and
sentence retrieval tasks for methods using similar visual
features.
1. Introduction
A good image description is often said to “paint a picture
in your mind’s eye.” The creation of a mental image may
play a significant role in sentence comprehension in humans
[15]. In fact, it is often this mental image that is remem-
bered long after the exact sentence is forgotten [29, 21].
What role should visual memory play in computer vision al-
gorithms that comprehend and generate image descriptions?
Recently, several papers have explored learning joint fea-
ture spaces for images and their descriptions [13, 32, 16].
These approaches project image features and sentence fea-
tures into a common space, which may be used for image
search or for ranking image captions. Various approaches
were used to learn the projection, including Kernel Canon-
ical Correlation Analysis (KCCA) [13], recursive neural
networks [32], or deep neural networks [16]. While these
approaches project both semantics and visual features to a
common embedding, they are not able to perform the in-
verse projection. That is, they cannot generate novel sen-
tences or visual depictions from the embedding.
In this paper, we propose a bi-directional representation
capable of generating both novel descriptions from images
and visual representations from descriptions. Critical to
both of these tasks is a novel representation that dynami-
cally captures the visual aspects of the scene that have al-
ready been described. That is, as a word is generated or
read the visual representation is updated to reflect the new
information contained in the word. We accomplish this us-
ing Recurrent Neural Networks (RNNs) [6, 24, 27]. One
long-standing problem of RNNs is their ...
HUMAN ACTION RECOGNITION IN VIDEOS USING STABLE FEATURES sipij
Human action recognition is still a challenging problem and researchers are focusing to investigate this
problem using different techniques. We propose a robust approach for human action recognition. This is
achieved by extracting stable spatio-temporal features in terms of pairwise local binary pattern (P-LBP)
and scale invariant feature transform (SIFT). These features are used to train an MLP neural network
during the training stage, and the action classes are inferred from the test videos during the testing stage.
The proposed features well match the motion of individuals and their consistency, and accuracy is higher
using a challenging dataset. The experimental evaluation is conducted on a benchmark dataset commonly
used for human action recognition. In addition, we show that our approach outperforms individual features
i.e. considering only spatial and only temporal feature.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Multiple Style-Transfer in Real-Time
1. Multiple Style-Transfer in Real-Time
Michael Maring
University of Michigan
Ann Arbor, MI
mykl@umich.edu
Kaustav Chakraborty
University of Michigan
Ann Arbor, MI
vatsuak@umich.edu
Abstract
Style transfer aims to combine the content of one im-
age with the artistic style of another. It was discovered that
lower levels of convolutional networks captured style infor-
mation, while higher levels captures content information.
The original style transfer formulation used a weighted
combination of VGG-16 layer activations to achieve this
goal. Later, this was accomplished in real-time using a
feed-forward network to learn the optimal combination of
style and content features from the respective images. The
first aim of our project was to introduce a framework for
capturing the style from several images at once. We propose
a method that extends the original real-time style transfer
formulation by combining the features of several style im-
ages. This method successfully captures color information
from the separate style images. The other aim of our project
was to improve the temporal style continuity from frame to
frame. Accordingly, we have experimented with the tem-
poral stability of the output images and discussed the var-
ious available techniques that could be employed as alter-
natives.
1. Introduction
1.1. Style Transfer Background
Style transfer was introduced by Gatys et al [1] as a gen-
erative method to combine the loss from different layers of
a convolutional neural network trained on image recogni-
tion. The authors noticed that the filters contained in the
lower layers of the VGG-16 network captured information
that closely represented the style of an image. Similarly, the
filters contained in higher layers captured more abstact in-
formation that could be interpreted as image content. Their
original idea was to create a new image that minimized the
combination of style and content loss that they obtained
from the VGG-16 network.
The process in [1] was iterative and so could not be ac-
complished in real-time. Johnson et al [2] accomplished
real-time style transfer be introducing a feed forward con-
volutional neural network that learned the optimal combi-
nation of style and content. Once training was complete,
the feed-forward network could be used to directly styl-
ize a new image. This method was improved by Ulyanov
et al [3], who noticed that replacing the transformer net-
work’s batch normalization layers with instance normaliza-
tion layers improved the visual quality of the style transfer.
The intuition of why instance normalization improved upon
batch normalization was explained well in Huang et al [4].
They determined that, ”Each single sample [content image],
however, may still have different styles.” Each content im-
age within the training batch has its own inherent style, and
batch normalization of several content images would create
some incoherent mixture of these during training.
1.2. Multiple Style Transfer Motivation
The real-time style transfer implementation introduced
in Johnson et al [2] can only be trained for one style at a
time. This was our motivation behind trying to combine
the information from multiple style images. Concretely, the
first aim of this paper was to simultaneously minimize the
loss function of the content image and several style images.
1.3. Style Continuity Background
The effect of running the transformer-net as a stan-
dalone feed forward network(with no grad-computation) af-
ter training provided fast computation results [2]. How-
ever this process produced temporal inconsistencies. This
was a similar problem in the case of image segmentation
which required the output to be penalized based on the in-
correct pixel labelling with respect to its neighbours. [5]
was one of the first papers to incorporate the concept of a
conditional-random field (CRF) that could be trained end-
to-end in the form of an recurrent neural network (RNN).
It yielded better results than even post possessing the image
labels through a CRF using the predictions made by the neu-
ral network as a prior. This was seen as a possible solution
to reduce the temporal inconsistencies in the images. Yet
another method that could be used to reduce computational
1
2. complexity is the reduction of trainable parameters by either
compressing the bulky network into a much portable form
that is more suitable for deployment [6], or designing net-
works that perform convolution with lesser parameters or
less tunable weights [7, 8].These networks perform similar
to the original less-compact version network however the
computing cost is highly reduced and as a result the train-
ing time can be substantially reduced in-turn we speculate a
faster evaluation module.
1.4. Style Continuity Motivation
Like in every other CNN algorithm the strive in the com-
munity always remain to make the process computationally
inexpensive or in real time. This sometimes takes its toll
on the network’s accuracy or visual quality. We wanted to
make sure that the computation of the results is fast enough
and additionally, it should be stable with less jittery outputs.
This called for including a form of constraint between the
styling of two separate images in the sequence.
1.5. Related Work
1.5.1 Arbitrary Style Transfer
The most significant limitation of the real-time style transfer
implementation in Johnson et al [2] was a new transformer
network had to be trained for each style. There have been
several real-time style transfer methods that aim to create
a high-dimensional, learned representation of style, which
would allow them to incorporate this information into a
transformer network to output a content image in any de-
sired (”arbitrary”) style [9, 4, 10]. Dumoulin et al [9] dis-
covered that styles share many artistic qualities, and that this
similarity can be represented in a high dimensional embed-
ding space. They successfully created a high-dimensional
embedding space learned from 32 distinct artistic styles and
demonstrated the possibility of combining artistic styles by
combining their embedding features in an intelligent way.
Huang et al discovered that they were able to render and
image in an arbitrary style by aligning the mean and vari-
ance of the content and style images using what they called
Adaptive Instance Normalization, or AdaIn, layers. Lastly,
Ghiasi et al [10] trained a network to create an embed-
ding space for describing style images using approximately
80,000 patintings. They were able to show that their embed-
ding space grouped together similar styles and allow them
to render content images in unique styles that were not used
during training.
1.5.2 Style Continuity
One of the notable methods that has been used to preserve
the consistency of the images after their transformation is
the use of CRFs. Conditional random fields, apply a layer
of pairwise loss in addition to the per-pixel loss. The main
objective function that CRFs try to minimize is written in
the form of a Gibbs per pixel energy of the assignment as,
E(x) =
i
ψu(xi) +
i<j
ψp(xi.xj)
which is comprised of the unary component ψu(xi), that
accounts for the cost/loss of assigning a style (in our case)
xi to the pixel i. while the pair wise energy is computed by
the second term. [5] took this process one step further and
was abe to model the CRF into an RNN allowing the pa-
rameters to be learing while training. This was employed
by the using the mean field iteration technique which is
essentially a way to employ the KL divergence in a more
tractable manner. Another inspiring model was that of the
Unet [11]. This is again another example of how techniques
that are applied in the image segmentation domain can be
cross over to our application. The image is downsampled
to break it down to its essential representative features and
then is upsampled/reconstructed back up again. This is a
pretty similar approach that we take in the transformer net-
work albeit with a number of modifications. An interesting
feature of the UNet is the ”crop -and copy” functionality as
shown in Fig. 1 , where the output from previous layers are
concatenated with the outputs from subsequent layers. This
was thought of as a striking feature that could probably al-
low us to extend the concept of temporal pixel relationship
a bit further. Another such method that has been popular
Figure 1: Original Unet Formulation. The arrows going
across the network show the crop and copy concept.
in the context of accounting for temporal changes is Long-
Short-Term Memory(LSTM)[12]. This technique was in-
vented to be useful in handling time series data specially for
the case of vanishing gradient or exploding gradient which
was a common issue while training RNNs. Finally, reduc-
tion of computation could be trace to the reduction in the
number of parameters requiring to be trained. Iandola et al
[7] proposed the SqueezeNet where they have made use of
3. a FireModule to reduce the number of weights requiring to
be trained. Here the authors achieve a network as efficient
as the famous AlexNet however using 500 times lesser pa-
rameters. These FireModules replace the 3x3 convolutions
with 1x1 convolutons. In our work we have tried to take
some features from FireModule and the UNet architecture
to reduce the computation time of training of our network.
2. Methods
2.1. Combining Style and Content
The original formulation of style transfer proposed by
Gatys et al [1] described the loss function as a weighted
combination of content loss and style loss. That is,
Ltotal = δcontent · Lcontent + δstyle · Lstyle (1)
The VGG-16 network that was used to define the percep-
tual losses is composed of five convolutional blocks sepa-
rated by maxpool layers. The activations used to define the
content and style losses were taken from the ReLU layer im-
mediately preceding the maxpool layers because they rep-
resented the activation at each spatial scale in the VGG net-
work. The content loss is given by the following,
Lc = ||φl(ˆy) − φ(yc)||2 (2)
where φl() is the activation of the ReLU layer from the lth
convolutional block of the VGG-16 network (used to de-
fine perceptual losses), ˆy is the output image of our trans-
former network, and yc is the content image. This loss is
unchanged in our implementation. We used the output of
the second convolutional block (i.e. l = 2) to define our
content loss as done in [2].
The style loss is given by the following,
Ls = ||
l
(Gl(ˆy) − Gl(ys))||2
F (3)
where Gl() is the gram matrix in [1, 2] and ys is the style
image. The gram matrix is the inner product of the filters
for a particular style layer.
Gl,(i,j) = Fl,i · Fl,j (4)
The (i, j) position of the gram matrix for layer l is given
by the dot product between the ith
filter, Fl,i, and jth
fil-
ter, Fl,j. If there are several style layers, the overall style
loss is the summation of each style layer loss. We used
the activations from convolutional blocks one through four,
l = {1, 2, 3, 4}, to minimize the style loss across several
spatial scales as done in [2].
2.2. Combining Multiple Styles
In order to combine several styles, the natural extension
of the original method was to incorporate the weighted sum
of losses from multiple style images. This is given by the
following,
Ls =
1
n
(Ls1
+ Ls2
+ . . . Lsn
) (5)
where Ls1
is the loss described above in Eq. 3 for style
image one. This was the new objective function that we
used to combine the features from several style images.
Further, it is possible to include a weighted sum of each
style image. For example, if there were two style images,
Ls =
1
n
(α · Ls1 + (1 − α) · Ls2 ) (6)
This would allow us to vary the contribution of each style
image to the overall transfer; effectively interpolating be-
tween styles.
The hyperparameters used for training are summarized
in Table 1. Unless otherwise stated, these are the parameters
that we used for training.
Parameter
Content Weight 1e5
Style Weight 1e10
Content Layers 2
Style Layers 1-4
Epochs 1
batch size 4
Learning Rate 1e-3
Optimizer Adam
Table 1: Summary of Hyperparameters
2.3. Dataset
Our network was trained using the Common Objects in
COtext, or COCO 1
, dataset. More specifically we used the
2014 training images. This dataset contains 83,000 images
of everyday objects such as food, people, household furni-
ture. While this dataset contains segmentation information,
it is not useful for this application. We needed a large corpus
of images so that our transformer network can successfully
apply the style in a variety of circumstances.
2.4. Transformer-Net
The transformer net is one the main features of [2]. The
main objective of the network is to act as the feed-forward
network that can quickly compute the optimization for the
style transfer. [3] showed that style transfer performed even
better when the batch normalization is replaced by instance
normalization. The detailed network is shown in Fig. 3. It is
a typical encoder decoder network as show in the represen-
tative network in the left bottom. First there are a number of
1http://cocodataset.org/#home
4. Figure 2: This depicts a style image rendered for styles 1 and 2. The last column shows the content image rendered using
our method for combining multiple styles. This combination rendering captures some color information from both styles but
very little, if any, texture from either style.
Figure 3: The transform net used in our pipeline. Top-
shows all the layers. Bottomleft shows the representative
”hourglass” structure. Bottom right is the key.
downsamplling steps which is followed by a bottleneck and
futher by the umsampling process. Most of the convolu-
tions are standard 3x3 or 9x9 kernels with strides of either 1
or 2 as is the case for upsampling or downsampling. ReLU
is used as the activation function. We have used reflection
padding in our network to minimize artifacts near the edge
caused by zero-padding. We now focus on a few impor-
tant parts in the network, namely the instance norm and the
ResNet.
2.4.1 Instance Norm
The main objective of normalization is to reduce the so
called covariance shift that resets the data to a zero mean
and unit covariance every time it is fed to a new layer. This
allows for the layer to learn more independently and also
allows for faster convergence. Batch normalization is com-
puted over the whole batch of the image while the instance
norm is the one that is computed only over a specific in-
stance in the input. This can be represented as,
ytijk =
xtijk − µti
σ2
ti +
, µti =
1
HW
W
i=1
H
m=1
xtilm
σ2
ti =
1
HW
W
i=1
H
m=1
(xtijk − µti)
2
where the t,i,j,k is are the tensor, channel, abscissa and or-
dinate indices respectively. The σ2
is the instance variance
while µ is the instance mean respectively, while y is the out-
put.This choice of normalization prevents the instance spe-
cific mean and co-variance shift over simplifying the learn-
ing procedure. Instance-norm is used both during the train-
ing as well as during the testing.
5. 2.4.2 ResNet
ResNets [13] were discovered as one of the processes to
reduce the problem of vanishing gradient allowing us to
train much deeper networks. ResNets essentially allow us
to easily learn the identity function and make sure that the
losses are not saturated over the length of the network. Our
network uses 5 ResNet blocks inside the bottleneck region.
This allows us to effectively train the 20-layer network. [13]
2.5. Correlation of the features
2.6. Style Consistency
Figure 4: The modified Transform net used in our pipeline.
Fig. 4 is the modified version of the transformer net
that we have experimented our style consistency approaches
on. This method tries to combine the novelties of both the
UNet[11] as well as that of the FireModule[7]. We will just
resize and copy the frame that is given as the input(which
will act as the stylized image at time t) to the transformer
network and merge it with the output of the transformer net-
work. This achieved by the 1x1 convolutions that has been
so effective in the case of Firemodules. The process is rel-
atively fast since it does not have to many parameters and
it just has to learn an identity function just like the ResNet
which additionally helps us even in the case of vanishing
gradients.
3. Results and Discussion
3.1. Combining Multiple Styles
The first experiment we performed was to combine the
style from two paintings that were visually very different.
The thought process behind this experiment was that the
output should show very different styles and the outcome
would be obvious. These two paintings were Weeping
Woman by Picasso (Style 1) and Woman With a Parasol by
Monet (Style 2). The stylized images are shown in Fig. 2.
The resultant stylization was barely able to capture the color
information of the two styles. The style loss dominated the
total loss during training for combining both styles (Fig. 5),
which was indicative of the inability to combine the high-
level perceptual features from the VGG network. For ex-
Figure 5: Training losses for one style (Picasso) and both
styles (Picasso and Monet). The style loss dominated the
total loss for combining both styles. This change can be
attributed to the failure to rectify the high-level features for
both styles.
ample, the style for training one style was around the same
magnitude as the content loss (Fig. 5).
Next, we experimented with the style weighting to see if
we could resolve the high style loss issue. We thought that
if we increased the style weighting, that the network would
learn how to better resolve the discrepancies between the
style features of both paintings. The stylizations with sev-
eral different style weightings are shown Fig. 7 (default
style weight = 1e10). We were not able to achieve a styl-
ization of the content image that successfully combined the
color and texture from both styles. In fact, when the style
weight was increased, triangular-shaped artifacts started to
appear within the frame (style weight = 1e11). Therefore,
we decided to test our method on two paintings that were
visually more similar.
The original experiment was reran with two new paint-
ings. The two paintings were the Scream by Munch (Style
1) and Starry Night by Van Gogh (Style 2). The stylization
of these images is shown in Fig. 8. The resulting styliza-
tions successfully combined the colors of the two paintings
as before, but also included small textures such as the ”sun-
like” objects from Starry Night. This improvement was re-
flected in the lower style loss for ”both styles” in Fig. 6 as
compared to Fig. 5.
We also tested the ability of our network to interpolate
between styles using Eq. 6. Our results for style inter-
6. Figure 6: Convergence of total, style, and content losses
during training for similar styles (Scream and Starry Night).
For the default style weight (1e10), the style loss was much
less than for two perceptually very different paintings Fig.
5. This indicated that the network was able to better com-
bine high-level features from both paintings.
.
polation are displayed in Fig. 9. It is clear that α = 0.7
incorporates more color from style 1 while α = 0.3 incor-
porates more color from style 2. Further, on the door frame
of α = 0.7, there are waves reminiscent of the textures from
style 1. Similarly, for α = 0.3, there are more ”sun-like”
features that are reminiscent of style 2.
While our method for combining multiple styles was
successful in capturing details from two perceptually sim-
ilar paintings, it failed to rectify differences between two
dissimilar paintings. This was likely due to the inability of
the high-level features used in VGG network to describe
the ”style” in these two paintings in a meaningful way.
This provides motivation to use methods described in the
related works section where they created a network to se-
mantically describe paintings in a higher dimensional space
[10]. Further, they papers were able to train their network
on many styles, rather than just two in our case. Any of
these methods may serve as an inspiration to improve upon
our method.
3.2. Style Consistancy
In order to check our network for style consistencies we
fed the network a stream of the same images after training.
Any change in the style element in the output would be con-
sidered a not a consistent result. Fig 10 shows the result of
how our architecture holds up. Fig 10a shows the output
of the original image used as the base line Fig 10b shows
the output without any from of correlation performed while
Fig 10c shows the result of the correlated network. Each
of the images show a zoomed in portion of their top left
area in order to display and compare the artifacts properly.
It can be seen that Fig 10a and Fig 10c shows pretty sim-
ilar results while Fig 10b artifacts deviate a lot. By this
we say that the output of the first and the third images are
consistent and they do not vary with time. We can loosely
claim that the style of our network completely depend on
the ”local-content” that it is focused on. If the local-content
remains unchanged so do the style. This can further be used
a shallow example of time independency of our network.
However, it must be declared that the network is really in-
consistent. Some of the outputs that we received were not
even images, they were just random number. Sometimes it
was just a white color and no content. This was one of the
very few correct outputs that we had received. The
4. Future Work
Another method we thought of that may be able to com-
bine style and content information is based upon the work
of [6]. This ”distillation” method was intended to combine
the information learned from one large network or an en-
semble of networks (”learned network”) into one, possible
smaller, network (”new” network). Within this paradigm,
the ”learned” networks would be the transformer networks
trained on fixed individual styles and the ”new” network
would be the transformer network to combine several styles.
For the style consistencies, a lot of research has to be done
to figure out why the outputs are not uniform. Addition-
ally, implementation of the LSTM maybe considered as a
long term goal or a CRF-RNN network to better preserve
consistencies.
4.1. Original Contributions
Our first contribution was the idea to combine multiple
styles in one training. Secondly, we developed a method
to interpolate between styles. Concretely, we coded from
scratch the transformer network and function to return the
VGG network activations. The transformer network struc-
ture we used was the same as Johnson et al [2] 2
. Their sup-
plementary material was a useful resource to determine the
exact structure of their transformer network. The training
script used to train our transformer network was a heavily
modified version of the script from the Pytorch examples
for real-time style transfer 3
. In particular, we altered the
script to mesh with our implementation of the VGG loss
2https://github.com/jcjohnson/fast-neural-style
3https://github.com/pytorch/examples/tree/
master/fast_neural_style
7. Figure 7: This illustrates stylizations similar to Fig. 2, but with different style loss weightings. There is a clear trend that as
the style weight increases, the content image is rendered more abstractly. Further, for style loss weighting 1e11, triangular-
shaped artifacts start appearing.
Figure 8: This depicts a style image rendered for styles 1 and 2. The last column shows the content image rendered using
our method for combining multiple styles. This combination rendering captures color information from both styles as well
as some textures. For example, there are ”sun-like” patterns that mimic Style 2.
8. Figure 9: This demonstrates the ability to interpolate between styles based upon different weighted contributions to the total
style loss as described in Eq. 6. There is a clear transition from more orange on the left to more blue on the right; mimicking
the style transition from Scream (style 1) to Starry Night (style 2).
Figure 10: Image Consistency results.
and transformer network. We also altered the script to al-
low for our multiple styles method and style interpolation
method. Additionally, we altered it to save training loss,
plot training loss (during training), and save a log file of the
hyperparameters used. We created bash utility scripts to al-
low us to train several networks and stylize several images
all at once. We created python scripts using OpenCV to
stylize images from a video file and render images from a
webcam in real time using our trained models. Every styl-
ized image presented in the results section is from a model
we trained using our code.
9. References
[1] Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.
A neural algorithm of artistic style. CoRR, abs/1508.06576,
2015. 1, 3
[2] Justin Johnson, Alexandre Alahi, and Fei-Fei Li. Percep-
tual losses for real-time style transfer and super-resolution.
CoRR, abs/1603.08155, 2016. 1, 2, 3, 6
[3] Dmitry Ulyanov, Andrea Vedaldi, and Victor S. Lempitsky.
Instance normalization: The missing ingredient for fast styl-
ization. CoRR, abs/1607.08022, 2016. 1, 3
[4] Xun Huang and Serge J. Belongie. Arbitrary style trans-
fer in real-time with adaptive instance normalization. CoRR,
abs/1703.06868, 2017. 1, 2
[5] Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-
Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang
Huang, and Philip HS Torr. Conditional random fields as
recurrent neural networks. In Proceedings of the IEEE inter-
national conference on computer vision, pages 1529–1537,
2015. 1, 2
[6] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distill-
ing the knowledge in a neural network. arXiv preprint
arXiv:1503.02531, 2015. 2, 6
[7] Forrest N Iandola, Song Han, Matthew W Moskewicz,
Khalid Ashraf, William J Dally, and Kurt Keutzer.
Squeezenet: Alexnet-level accuracy with 50x fewer pa-
rameters and¡ 0.5 mb model size. arXiv preprint
arXiv:1602.07360, 2016. 2, 5
[8] Chai Wah Wu. Prodsumnet: reducing model parameters in
deep neural networks via product-of-sums matrix decompo-
sitions. arXiv preprint arXiv:1809.02209, 2018. 2
[9] Vincent Dumoulin, Jonathon Shlens, and Manjunath Kud-
lur. A learned representation for artistic style. CoRR,
abs/1610.07629, 2016. 2
[10] Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent
Dumoulin, and Jonathon Shlens. Exploring the structure of a
real-time, arbitrary neural artistic stylization network. CoRR,
abs/1705.06830, 2017. 2, 6
[11] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-
net: Convolutional networks for biomedical image segmen-
tation. In International Conference on Medical image com-
puting and computer-assisted intervention, pages 234–241.
Springer, 2015. 2, 5
[12] Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term
memory. Neural computation, 9(8):1735–1780, 1997. 2
[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.
Deep residual learning for image recognition. In Proceed-
ings of the IEEE conference on computer vision and pattern
recognition, pages 770–778, 2016. 5