[PR12] Spectral Normalization for Generative Adversarial Networks

Graph Attention Network

Takahiro Kubo

On the Convergence of Adam and Beyond

harmonylab

Introduction to Graph neural networks @ Vienna Deep Learning meetup

Liad Magen

Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.

[DL輪読会]Learning to Generalize: Meta-Learning for Domain Generalization

PEGASUS is a large Transformer-based model for abstractive text summarization. It uses a novel pre-training objective called gap-sentence generation (GSG) which masks sentences from input documents and trains the model to generate the missing sentences. GSG more closely resembles the downstream summarization task compared to other objectives. In experiments, PEGASUS achieved state-of-the-art results on 12 summarization datasets using GSG pre-training and outperformed other models when fine-tuned on limited data.

Disentanglement Survey:Can You Explain How Much Are Generative models Disenta...

Hideki Tsunashima

【DL輪読会】Where do Models go Wrong? Parameter-Space Saliency Maps for Explainabi...

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ssuser5861ac

Sliced Wasserstein距離と生成モデル

ohken

This document discusses various methods for calculating Wasserstein distance between probability distributions, including: - Sliced Wasserstein distance, which projects distributions onto lower-dimensional spaces to enable efficient 1D optimal transport calculations. - Max-sliced Wasserstein distance, which focuses sampling on the most informative projection directions. - Generalized sliced Wasserstein distance, which uses more flexible projection functions than simple slicing, like the Radon transform. - Augmented sliced Wasserstein distance, which applies a learned transformation to distributions before projecting, allowing more expressive matching between distributions. These sliced/generalized Wasserstein distances have been used as loss functions for generative models with promising

（DL輪読）Variational Dropout Sparsifies Deep Neural Networks

Masahiro Suzuki

[DL輪読会]SeqGan Sequence Generative Adversarial Nets with Policy Gradient

[DL輪読会]ICLR2020の分布外検知速報

【論文紹介】 Attention Based Spatial-Temporal Graph Convolutional Networks for Traf...

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[DL輪読会]Image-to-Image Translation with Conditional Adversarial Networks

汎化性能測定

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Interpretability beyond feature attribution quantitative testing with concept...

MLconf

TCAV is a method for interpreting machine learning models by quantitatively measuring the importance of user-chosen concepts for a model's predictions, even if those concepts were not part of the model's training data or input features. It does this by learning concept activation vectors (CAVs) that represent concepts and using the CAVs to calculate a model's sensitivity or importance to each concept via directional derivatives. TCAV was shown to validate ground truths from sanity check experiments, uncover geographical biases in widely used models, and match domain expert concepts for diabetic retinopathy versus those a model may use, helping ensure models' values and knowledge are properly aligned and reflected.

Image style transfer & AI on App

Chihyang Li

The document discusses image style transfer and machine learning applications for iOS. It introduces neural style transfer using convolutional neural networks. It describes using feature maps from VGG networks to calculate content and style losses for transferring style between images. It also discusses using CoreML and Apple's Vision framework to easily integrate pre-trained models into iOS apps for tasks like face detection, object recognition and style transfer.

[DL輪読会]逆強化学習とGANs

[DL輪読会]Randomized Prior Functions for Deep Reinforcement Learning

Deep Q-Network　論文輪読会

Kotaro Tanahashi

NLP2023 緊急パネル：ChatGPTで自然言語処理は終わるのか？説明スライド

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[DL輪読会]"CyCADA: Cycle-Consistent Adversarial Domain Adaptation"&"Learning Se...

1. Two papers on unsupervised domain adaptation were presented at ICML2018: "Learning Semantic Representations for Unsupervised Domain Adaptation" and "CyCADA: Cycle-Consistent Adversarial Domain Adaptation". 2. The CyCADA paper uses cycle-consistent adversarial domain adaptation with cycle GAN to translate images at the pixel level while also aligning representations at the semantic level. 3. The semantic representation paper uses semantic alignment and introduces techniques like adding noise to improve over previous semantic alignment methods.

Generative Adversarial Imitation Learningの紹介（RLアーキテクチャ勉強会）

Yusuke Nakata

SSII2019TS: Shall We GANs? ～GANの基礎から最近の研究まで～

SSII

SSII2019 チュートリアル講演 TS1 6月12日(水) 9:45～11:05 (メインホール) Generative Adversarial Nets (GAN) は、提案されて以降、画像生成を中心に応用がなされ、また同時に理論的面も研究がなされてきた。本チュートリアルにおいては、まずGANの基本的な事項について述べる。GAN は「二つの分布を一致させる」ものであるが、提案手法を用いることでそれが達成できることを確認し、簡単な場合の数値計算結果を例示する。その後、時間の限り、GAN の応用例や、GAN の問題点とそれに対する最近の研究を紹介する。

PR-231: A Simple Framework for Contrastive Learning of Visual Representations

Jinwon Lee

The document presents SimCLR, a framework for contrastive learning of visual representations using simple data augmentation. Key aspects of SimCLR include using random cropping and color distortions to generate positive sample pairs for the contrastive loss, a nonlinear projection head to learn representations, and large batch sizes. Evaluation shows SimCLR learns representations that outperform supervised pretraining on downstream tasks and achieves state-of-the-art results with only view augmentation and contrastive loss.

Neural Network Based Individual Classification System

This document describes a neural network model developed for individual classification. The model was designed to measure personality traits through a questionnaire. It then uses a neural network trained on sample data through unsupervised and supervised learning with multi-layer perceptions. The backpropagation algorithm was used to train the network. The neural network architecture included multiple neuron layers trained on a 200 item data set, achieving 99.82% accuracy. The goal was to classify individuals into high, middle, and low personality categories for use in job selection or training.

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Pegasus

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ssuser5861ac

Sliced Wasserstein距離と生成モデル

ohken

（DL輪読）Variational Dropout Sparsifies Deep Neural Networks

Masahiro Suzuki

[DL輪読会]SeqGan Sequence Generative Adversarial Nets with Policy Gradient

[DL輪読会]ICLR2020の分布外検知速報

【論文紹介】 Attention Based Spatial-Temporal Graph Convolutional Networks for Traf...

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[DL輪読会]Image-to-Image Translation with Conditional Adversarial Networks

汎化性能測定

Masanori Yamada

Interpretability beyond feature attribution quantitative testing with concept...

MLconf

Image style transfer & AI on App

Chihyang Li

[DL輪読会]逆強化学習とGANs

[DL輪読会]Randomized Prior Functions for Deep Reinforcement Learning

Deep Q-Network　論文輪読会

Kotaro Tanahashi

NLP2023 緊急パネル：ChatGPTで自然言語処理は終わるのか？説明スライド

JunSuzuki21

[DL輪読会]"CyCADA: Cycle-Consistent Adversarial Domain Adaptation"&"Learning Se...

Generative Adversarial Imitation Learningの紹介（RLアーキテクチャ勉強会）

Yusuke Nakata

SSII2019TS: Shall We GANs? ～GANの基礎から最近の研究まで～

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What's hot (20)

Pegasus

Disentanglement Survey:Can You Explain How Much Are Generative models Disenta...

【DL輪読会】Where do Models go Wrong? Parameter-Space Saliency Maps for Explainabi...

論文要約：AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERT...

Sliced Wasserstein距離と生成モデル

（DL輪読）Variational Dropout Sparsifies Deep Neural Networks

[DL輪読会]SeqGan Sequence Generative Adversarial Nets with Policy Gradient

[DL輪読会]ICLR2020の分布外検知速報

【論文紹介】 Attention Based Spatial-Temporal Graph Convolutional Networks for Traf...

[DL輪読会]Image-to-Image Translation with Conditional Adversarial Networks

汎化性能測定

Interpretability beyond feature attribution quantitative testing with concept...

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Deep Q-Network　論文輪読会

NLP2023 緊急パネル：ChatGPTで自然言語処理は終わるのか？説明スライド

[DL輪読会]"CyCADA: Cycle-Consistent Adversarial Domain Adaptation"&"Learning Se...

Generative Adversarial Imitation Learningの紹介（RLアーキテクチャ勉強会）

SSII2019TS: Shall We GANs? ～GANの基礎から最近の研究まで～

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PR-231: A Simple Framework for Contrastive Learning of Visual Representations

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Unit-4 PART-4 Overfitting.pptx

Sandeep Kumar

Regularization techniques like dropout and max-norm regularization help reduce overfitting in deep learning models. Dropout prevents overfitting by randomly ignoring nodes during training, ensuring nodes are not codependent. Max-norm regularization constrains weight vectors to have an L2 norm below a set value r to prevent large weights that overly fit the training data. Both techniques improve model generalization to unseen data compared to non-regularized networks, though dropout training may take longer.

Evolutionary Algorithm for Optimal Connection Weights in Artificial Neural Ne...

CSCJournals

A neural network may be considered as an adaptive system that progressively self-organizes in order to approximate the solution, making the problem solver free from the need to accurately and unambiguously specify the steps towards the solution. Moreover, Evolutionary Artificial Neural Networks (EANNs) have the ability to progressively improve their performance on a given task by executing learning. An evolutionary computation gives adaptability for connection weights using feed forward architecture. In this paper, the use of evolutionary computation for feed-forward neural network learning is discussed. To check the validation of proposed method, XOR benchmark problem has been used. The accuracy of the proposed model is more satisfactory as compared to gradient method.

decorrelated batch normalization(cvpr2018)

ali hassan

1. Decorrelated Batch Normalization (DBN) whitens activations within each layer using ZCA whitening to avoid stochastic axis swapping caused by PCA whitening. 2. DBN improves conditioning and speeds up convergence compared to Batch Normalization by achieving approximate dynamical isometry and diagonalizing the Fisher Information Matrix. 3. Experiments on multilayer perceptrons and convolutional neural networks show that DBN outperforms Batch Normalization, and can be used to train deeper and wider residual networks.

Comparison of Neural Network Training Functions for Hematoma Classification i...

IOSR Journals

Classification is one of the most important task in application areas of artificial neural networks (ANN).Training neural networks is a complex task in the supervised learning field of research. The main difficulty in adopting ANN is to find the most appropriate combination of learning, transfer and training function for the classification task. We compared the performances of three types of training algorithms in feed forward neural network for brain hematoma classification. In this work we have selected Gradient Descent based backpropagation, Gradient Descent with momentum, Resilence backpropogation algorithms. Under conjugate based algorithms, Scaled Conjugate back propagation, Conjugate Gradient backpropagation with Polak-Riebreupdates(CGP) and Conjugate Gradient backpropagation with Fletcher-Reeves updates (CGF).The last category is Quasi Newton based algorithm, under this BFGS, Levenberg-Marquardt algorithms are selected. Proposed work compared training algorithm on the basis of mean square error, accuracy, rate of convergence and correctness of the classification. Our conclusion about the training functions is based on the simulation results

Neural networks for the prediction and forecasting of water resources variables

Jonathan D'Cruz

This document reviews the use of artificial neural networks (ANNs) for predicting and forecasting water resource variables. It outlines the key steps in developing ANN models, including choosing performance criteria, preprocessing and dividing data, determining appropriate model inputs and network architecture, optimizing connection weights through training, and validating models. Specifically, it focuses on feedforward networks with sigmoid transfer functions, which have been most widely used for predicting water resources variables.

Hybrid Quantum Genetic Particle Swarm Optimization Algorithm For Solving Opti...

paperpublications3

Abstract: This paper presents hybrid particle swarm algorithm for solving the multi-objective reactive power dispatch problem. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. In this paper, a framework of hybrid particle swarm optimization algorithm, called Hybrid quantum genetic particle swarm optimization (HQGPSO), is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms. Keywords: quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm Optimization, Swarm Intelligence, optimal reactive power, Transmission loss.

part3Module 3 ppt_with classification.pptx

VaishaliBagewadikar

This document provides an overview of machine learning concepts related to supervised learning. It discusses perceptron models, which are simple linear classifiers that can be trained with algorithms like gradient descent. Gradient descent and stochastic gradient descent are described as methods for updating perceptron weights to minimize error. The document also introduces common supervised learning algorithms for classification tasks like naive Bayes, decision trees, logistic regression, and support vector machines. Popular machine learning applications that use classification are also mentioned.

Handling Imbalanced Data: SMOTE vs. Random Undersampling

This document compares different techniques for handling imbalanced data, including random undersampling and SMOTE (Synthetic Minority Over-sampling Technique), using two machine learning classifiers - Random Forest and XGBoost. It finds that XGBoost generally performs better than Random Forest across different sampling techniques in terms of metrics like AUC, sensitivity and specificity. Random undersampling with XGBoost provided the most balanced results on the randomly generated imbalanced dataset used in this study. SMOTE without proper validation gave the best numerical results but likely overfit the data.

A COMPARATIVE STUDY OF BACKPROPAGATION ALGORITHMS IN FINANCIAL PREDICTION

IJCSEA Journal

Stock market price index prediction is a challenging task for investors and scholars. Artificial neural networks have been widely employed to predict financial stock market levels thanks to their ability to model nonlinear functions. The accuracy of backpropagation neural networks trained with different heuristic and numerical algorithms is measured for comparison purpose. It is found that numerical algorithm outperform heuristic techniques.

PR-330: How To Train Your ViT? Data, Augmentation, and Regularization in Visi...

Jinwon Lee

The document summarizes a study on training Vision Transformers (ViTs) by exploring different combinations of data augmentation, regularization techniques, model sizes, and training dataset sizes. Some key findings include: 1) Models trained with extensive data augmentation on ImageNet-1k performed comparably to those trained on the larger ImageNet-21k dataset without augmentation. 2) Transfer learning from pre-trained models was more efficient and achieved better results than training models from scratch, even with extensive compute. 3) Models pre-trained on more data showed better transfer ability, indicating more data yields more generic representations.

Learning Sparse Networks using Targeted Dropout

Seunghyun Hwang

Targeted dropout is a technique that applies dropout primarily to network units and weights that are believed to be less useful based on their magnitudes. This makes networks robust to post-hoc pruning while achieving high sparsity. Experiments on ResNet, Wide ResNet and Transformer models on image and text tasks achieved up to 99% sparsity with less than 4% accuracy drop. Scheduling the targeting proportion and dropout rates over time was found to improve results compared to random pruning before training. Targeted dropout is an effective regularization method for training networks that can be heavily pruned after training.

A hybrid constructive algorithm incorporating teaching-learning based optimiz...

IJECEIAES

The document describes a hybrid algorithm that combines a modified multiple operations using statistical tests (MMOST) constructive algorithm with an improved teaching-learning based optimization (ITLBO) algorithm for neural network training. The hybrid algorithm simultaneously optimizes the neural network structure and weights. The MMOST algorithm constructs different network structures, while the ITLBO algorithm finds the optimal weights for each structure. The hybrid algorithm, called MCO-ITLBO, is tested on classification and time series prediction problems and is shown to outperform other algorithms in terms of error rates and network complexity. Experimental results demonstrate that the MCO-ITLBO algorithm provides better performance than algorithms using only constructive or training methods.

Jf3515881595

IJERA Editor

This document compares the performance of two neural network architectures, multi-layer perceptron (MLP) and radial basis function (RBF) networks, on a face recognition system. It trains MLP networks using different variants of the backpropagation algorithm and compares the results to RBF networks. The document finds that RBF networks provide better generalization performance compared to backpropagation algorithms and have faster training times, making them more suitable for face recognition.

IEEE 2015 Matlab Projects

Vijay Karan

Enhancing energy efficient dynamic load balanced clustering protocol using Dy...

IJTET Journal

Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc networks. MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load balanced clustering problem (DLBCP). It is necessary to have an effective clustering algorithm for adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load balance and energy metric in GA to solve the DLBCP. It is important to select the energy efficient cluster head for maintaining the cluster structure and balance the load effectively. Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed schemes increases the network life time and reduces the energy consumption.

IRJET- Analysis of Vehicle Number Plate Recognition

The Air Force Office of Scientific Research

This document summarizes research on techniques for enhancing license plate images and improving vehicle number plate recognition. It discusses using forward motion deblurring, scale-based region growing, blur estimation, blind image deblurring, and a no-reference metric to enhance license plate image details and obtain deblurred images. The document also reviews related work applying these and other techniques, such as binary SIFT, energy cooperation in wireless networks, and parametric blur estimation for natural image restoration. The goal is to develop more accurate and robust methods for automatic license plate recognition.

IEEE 2015 Matlab Projects

Vijay Karan

This document provides information on several 2015 IEEE Matlab projects related to signal processing and image analysis. It lists the project titles, languages, links, and abstracts for 10 different Matlab projects. The projects cover topics such as target source separation using deep neural networks, hyperspectral image classification using sparse representation, image denoising techniques, and cardiovascular biometrics.

Fahroo - Optimization and Discrete Mathematics - Spring Review 2013

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More from JaeJun Yoo

[PR12] Generative Models as Distributions of Functions

[CVPR2020] Simple but effective image enhancement techniques

The document discusses several image enhancement techniques: 1. WCT2, which uses wavelet transforms for photorealistic style transfer, achieving faster and lighter models than previous techniques. 2. CutBlur, a new data augmentation method that improves performance on super-resolution and other low-level vision tasks by adding blur and cutting patches from images. 3. SimUSR, a simple but strong baseline for unsupervised super-resolution that achieves state-of-the-art results using only a single low-resolution image during training.

Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Anal...

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1) The document discusses super-resolution techniques in deep learning, including inverse problems, image restoration problems, and different deep learning models. 2) Early models like SRCNN used convolutional networks for super-resolution but were shallow, while later models incorporated residual learning (VDSR), recursive learning (DRCN), and became very deep and dense (SRResNet). 3) Key developments included EDSR which provided a strong backbone model and GAN-based approaches like SRGAN which aimed to generate more realistic textures but require new evaluation metrics.

A beginner's guide to Style Transfer and recent trends

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.

Introduction to ambient GAN

This paper proposes AmbientGAN, which trains a generative adversarial network using partial or noisy observations rather than fully observed samples. AmbientGAN trains the discriminator on the measurement domain rather than the raw data domain, allowing the generator to be trained without needing large amounts of good training data. The paper proves it is theoretically possible to recover the original data distribution even when the measurement process is not invertible. It presents experimental results showing AmbientGAN can generate high quality samples and recover the underlying data distribution from various types of lossy and noisy measurements.

[PR12] categorical reparameterization with gumbel softmax

[PR12] understanding deep learning requires rethinking generalization

The document discusses a paper that argues traditional theories of generalization may not fully explain why large neural networks generalize well in practice. It summarizes the paper's key points: 1) The paper shows neural networks can easily fit random labels, calling into question traditional measures of complexity. 2) Regularization helps but is not the fundamental reason for generalization. Neural networks have sufficient capacity to memorize data. 3) Implicit biases in algorithms like SGD may better explain generalization by driving solutions toward minimum norm. 4) The paper suggests rethinking generalization as the effective capacity of neural networks may differ from theoretical measures. Understanding finite sample expressivity is important.

[PR12] Capsule Networks - Jaejun Yoo

The document discusses capsule networks, a type of neural network proposed by Geoff Hinton in 2017 as an alternative to convolutional neural networks (CNNs) for computer vision tasks. Capsule networks aim to address some limitations of CNNs, such as their inability to capture spatial relationships and pose information. The key concepts discussed include dynamic routing between capsules, which allows for parts-based representation, and equivariance, where capsules can learn transformation properties like position and orientation. The document provides an overview of a capsule network architecture and routing algorithm proposed in a 2017 paper by Sabour et al.

[PR12] Inception and Xception - Jaejun Yoo

This document discusses Inception and Xception models for computer vision tasks. It describes the Inception architecture, which uses 1x1, 3x3 and 5x5 convolutional filters arranged in parallel to capture correlations at different scales more efficiently. It also describes the Xception model, which entirely separates cross-channel correlations and spatial correlations using depthwise separable convolutions. The document compares different approaches for reducing computational costs like pooling and strided convolutions.

[PR12] PixelRNN- Jaejun Yoo

[Pr12] dann jaejun yoo

Introduction to domain adversarial training of neural network. (Kor) video : https://www.youtube.com/watch?v=n2J7giHrS-Y&t=1s Papers: A survey on transfer learning, SJ Pan 2009 / A theory of learning from different domains, S Ben-David et al. 2010 / Domain-Adversarial Training of Neural Networks, Y Ganin 2016 Slides I refered: http://www.di.ens.fr/~germain/talks/nips2014_dann_slides.pdf http://john.blitzer.com/talks/icmltutorial_2010.pdf (DA theory part) https://epat2014.sciencesconf.org/conference/epat2014/pages/slides_DA_epat_17.pdf (DA theory part) https://www.slideshare.net/butest/ppt-3860159 (DA theory part)

Variants of GANs - Jaejun Yoo

Picked-up lists of GAN variants which provided insights to the community. (GANs-Improved GANs-DCGAN-Unrolled GAN-InfoGAN-f-GAN-EBGAN-WGAN) After short introduction to GANs, we look through the remaining difficulties of standard GANs and their temporary solutions (Improved GANs). By following the slides, we can see the other solutions which tried to resolve the problems in various ways, e.g. careful architecture selection (DCGAN), slight change in update (Unrolled GAN), additional constraint (InfoGAN), generalization of the loss function using various divergence (f-GAN), providing new framework of energy based model (EBGAN), another step of generalization of the loss function (WGAN).

[PR12] intro. to gans jaejun yoo