https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
In recent years, deep learning approaches have come to dominate discriminative problems in many sub-areas of machine learning. Alongside this, they have also powered exciting improvements in generative and conditional modeling of richly structured data such as text, images, and audio. This talk, led by indico's Head of Research, Alec Radford, will serve as an introduction to several emerging application areas of generative modeling and provide a survey of recent techniques in the field.
Boston ML Forum 2016
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
요즘 Image관련 Deep learning 관련 논문에서 많이 나오는
용어인 Invariance와 Equivariance의 차이를 알기쉽게 설명하는 자료를 만들어봤습니다. Image의 Transformation에 대해
Equivariant한 feature를 만들기 위하여 제안된 Group equivariant Convolutional. Neural Networks 와 Capsule Nets에 대하여 설명
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Anomaly detection using deep one class classifier홍배 김
- Anomaly detection의 다양한 방법을 소개하고
- Support Vector Data Description (SVDD)를 이용하여
cluster의 모델링을 쉽게 하도록 cluster의 형상을 단순화하고
boundary근방의 애매한 point를 처리하는 방법 소개
In recent years, deep learning approaches have come to dominate discriminative problems in many sub-areas of machine learning. Alongside this, they have also powered exciting improvements in generative and conditional modeling of richly structured data such as text, images, and audio. This talk, led by indico's Head of Research, Alec Radford, will serve as an introduction to several emerging application areas of generative modeling and provide a survey of recent techniques in the field.
Boston ML Forum 2016
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data anlytics tools.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
Pascual, Santiago, Antonio Bonafonte, and Joan Serrà. "SEGAN: Speech Enhancement Generative Adversarial Network." INTERSPEECH 2017.
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.
요즘 Image관련 Deep learning 관련 논문에서 많이 나오는
용어인 Invariance와 Equivariance의 차이를 알기쉽게 설명하는 자료를 만들어봤습니다. Image의 Transformation에 대해
Equivariant한 feature를 만들기 위하여 제안된 Group equivariant Convolutional. Neural Networks 와 Capsule Nets에 대하여 설명
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
https://telecombcn-dl.github.io/dlai-2020/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates theprobabilitythatasamplecamefromthetrainingdataratherthan G. Thetraining procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1 2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
This presentation is for my Seminar Course at the University of Tehran. in this presentation, I will introduce some of the newest and also exciting developments in Generative Adversarial Networks.
Alpine ML Talk: Vtreat: A Package for Automating Variable Treatment in R By ...Chester Chen
VTREAT: A Package for Automating Variable Treatment in R
Data characterization, treatment, and cleaning are necessary (though not always glamorous) components of machine learning and data science projects. While there is no substitute for getting your hands dirty in the data, there are many data issues that repeat from project to project. In particular, how do you deal with missing data values? How do you deal with previously unobserved categorical values?
In this talk, I will discuss some typical data problems, and describe VTREAT, our in-progress R package for automating the treatment of these common data issues.
Bio:
Nina Zumel is a Principal Consultant with Win-Vector LLC, a data science consulting firm based in San Francisco. Her technical interests include data science, statistics, statistical learning, and data visualization. She is also the co-author with John Mount of Practical Data Science with R. This book presents the process and principles of data science from a practitioner’s perspective, and complements existing texts on machine learning, statistics, big data, and R.
Alberto Massidda - Scenes from a memory - Codemotion Rome 2019Codemotion
Generating representations is the ultimate act of creativity. Recent advancements in neural networks (and in processing power) brought us the capability to perform regression against complex samples like images and audio. In this presentation we show the underlying mechanics of media generation from latent space representation of abstract visual ideas, real embodiment of “Platonic” concepts, with Variational Autoencoders, Generative Adversarial Networks, neural style transfer and PixelRNN/CNN along with current practical applications like DeepFake.
This document provides an overview of deep generative learning and summarizes several key generative models including GANs, VAEs, diffusion models, and autoregressive models. It discusses the motivation for generative models and their applications such as image generation, text-to-image synthesis, and enhancing other media like video and speech. Example state-of-the-art models are provided for each application. The document also covers important concepts like the difference between discriminative and generative modeling, sampling techniques, and the training procedures for GANs and VAEs.
Machine translation and computer vision have greatly benefited from the advances in deep learning. A large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two fields in sign language translation and production still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses.
The transformer is the neural architecture that has received most attention in the early 2020's. It removed the recurrency in RNNs, replacing it with and attention mechanism across the input and output tokens of a sequence (cross-attenntion) and between the tokens composing the input (and output) sequences, named self-attention.
These slides review the research of our lab since 2016 on applied deep learning, starting from our participation in the TRECVID Instance Search 2014, moving into video analysis with CNN+RNN architectures, and our current efforts in sign language translation and production.
Machine translation and computer vision have greatly benefited of the advances in deep learning. The large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two field in sign language translation and production is still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses. This talk will present these challenges and the How2✌️Sign dataset (https://how2sign.github.io) recorded at CMU in collaboration with UPC, BSC, Gallaudet University and Facebook.
https://imatge.upc.edu/web/publications/sign-language-translation-and-production-multimedia-and-multimodal-challenges-all
https://imatge-upc.github.io/synthref/
Integrating computer vision with natural language processing has achieved significant progress
over the last years owing to the continuous evolution of deep learning. A novel vision and language
task, which is tackled in the present Master thesis is referring video object segmentation, in which a
language query defines which instance to segment from a video sequence. One of the biggest chal-
lenges for this task is the lack of relatively large annotated datasets since a tremendous amount of
time and human effort is required for annotation. Moreover, existing datasets suffer from poor qual-
ity annotations in the sense that approximately one out of ten language expressions fails to uniquely
describe the target object.
The purpose of the present Master thesis is to address these challenges by proposing a novel
method for generating synthetic referring expressions for an image (video frame). This method pro-
duces synthetic referring expressions by using only the ground-truth annotations of the objects as well
as their attributes, which are detected by a state-of-the-art object detection deep neural network. One
of the advantages of the proposed method is that its formulation allows its application to any object
detection or segmentation dataset.
By using the proposed method, the first large-scale dataset with synthetic referring expressions for
video object segmentation is created, based on an existing large benchmark dataset for video instance
segmentation. A statistical analysis and comparison of the created synthetic dataset with existing ones
is also provided in the present Master thesis.
The conducted experiments on three different datasets used for referring video object segmen-
tation prove the efficiency of the generated synthetic data. More specifically, the obtained results
demonstrate that by pre-training a deep neural network with the proposed synthetic dataset one can
improve the ability of the network to generalize across different datasets, without any additional annotation cost. This outcome is even more important taking into account that no additional annotation cost is involved.
Master MATT thesis defense by Juan José Nieto
Advised by Víctor Campos and Xavier Giro-i-Nieto.
27th May 2021.
Pre-training Reinforcement Learning (RL) agents in a task-agnostic manner has shown promising results. However, previous works still struggle to learn and discover meaningful skills in high-dimensional state-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational or contrastive techniques. We demonstrate that both allow learning a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. We also explore alternative rewards and input observations to overcome these limitations.
https://imatge.upc.edu/web/publications/discovery-and-learning-navigation-goals-pixels-minecraft
Peter Muschick MSc thesis
Universitat Pollitecnica de Catalunya, 2020
Sign language recognition and translation has been an active research field in the recent years with most approaches using deep neural networks to extract information from sign language data. This work investigates the mostly disregarded approach of using human keypoint estimation from image and video data with OpenPose in combination with transformer network architecture. Firstly, it was shown that it is possible to recognize individual signs (4.5% word error rate (WER)). Continuous sign language recognition though was more error prone (77.3% WER) and sign language translation was not possible using the proposed methods, which might be due to low accuracy scores of human keypoint estimation by OpenPose and accompanying loss of information or insufficient capacities of the used transformer model. Results may improve with the use of datasets containing higher repetition rates of individual signs or focusing more precisely on keypoint extraction of hands.
https://github.com/telecombcn-dl/lectures-all/
These slides review techniques for interpreting the behavior of deep neural networks. The talk reviews basic techniques such as the display of filters and tensors, as well as more advanced ones that try to interpret which part of the input data is responsible for the predictions, or generate data that maximizes the activation of certain neurons.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
https://telecombcn-dl.github.io/drl-2020/
This course presents the principles of reinforcement learning as an artificial intelligence tool based on the interaction of the machine with its environment, with applications to control tasks (eg. robotics, autonomous driving) o decision making (eg. resource optimization in wireless communication networks). It also advances in the development of deep neural networks trained with little or no supervision, both for discriminative and generative tasks, with special attention on multimedia applications (vision, language and speech).
Giro-i-Nieto, X. One Perceptron to Rule Them All: Language, Vision, Audio and Speech. In Proceedings of the 2020 International Conference on Multimedia Retrieval (pp. 7-8).
Tutorial page:
https://imatge.upc.edu/web/publications/one-perceptron-rule-them-all-language-vision-audio-and-speech-tutorial
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities.
Image segmentation is a classic computer vision task that aims at labeling pixels with semantic classes. These slides provide an overview of the basic approaches applied from the deep learning field to tackle this challenge and presents the basic subtasks (semantic, instance and panoptic segmentation) and related datasets.
Presented at the International Summer School on Deep Learning (ISSonDL) 2020 held online and organized by the University of Gdansk (Poland) between the 30th August and 2nd September.
http://2020.dl-lab.eu/virtual-summer-school-on-deep-learning/
https://imatge-upc.github.io/rvos-mots/
Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones.
Deep neural networks have achieved outstanding results in various applications such as vision, language, audio, speech, or reinforcement learning. These powerful function approximators typically require large amounts of data to be trained, which poses a challenge in the usual case where little labeled data is available. During the last year, multiple solutions have been proposed to leverage this problem, based on the concept of self-supervised learning, which can be understood as a specific case of unsupervised learning. This talk will cover its basic principles and provide examples in the field of multimedia.
Deep neural networks have revolutionized the data analytics scene by improving results in several and diverse benchmarks with the same recipe: learning feature representations from data. These achievements have raised the interest across multiple scientific fields, especially in those where large amounts of data and computation are available. This change of paradigm in data analytics has several ethical and economic implications that are driving large investments, political debates and sounding press coverage under the generic label of artificial intelligence (AI). This talk will present the fundamentals of deep learning through the classic example of image classification, and point at how the same principal has been adopted for several tasks. Finally, some of the forthcoming potentials and risks for AI will be pointed.
More from Universitat Politècnica de Catalunya (20)
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
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This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Deep Generative Models II (DLAI D10L1 2017 UPC Deep Learning for Artificial Intelligence)
1. [course site]
Santiago Pascual de la Puente
santi.pascual@upc.edu
PhD Candidate
Universitat Politecnica de Catalunya
Technical University of Catalonia
Deep Generative Models II
#DLUPC
4. Key Idea: our model cares about what distribution generated the input data
points, and we want to mimic it with our probabilistic model. Our learned
model should be able to make up new samples from the distribution, not
just copy and paste existing samples!
4
What is a generative model?
Figure from NIPS 2016 Tutorial: Generative Adversarial Networks (I. Goodfellow)
5. 5
Model the probability density function:
● Explicitly
○ With tractable density → PixelRNN, PixelCNN and
Wavenet
○ With approximate density → Variational
Auto-Encoders
● Implicitly
○ Generative Adversarial Networks
Taxonomy
6. PixelCNN: Factorizing the joint distribution
● Model explicitly the joint probability distribution of data streams x as a product of
element-wise conditional distributions for each element xi
in the stream.
○ Example: An image x of size (n, n) is decomposed scanning pixels in raster mode
(Row by row and pixel by pixel within every row)
○ Apply probability chain rule: xi
is the i-th pixel in the image.
6
10. 10
Generative Adversarial Networks (GANs)
We have two modules: Generator (G) and Discriminator (D).
● They “fight” against each other during training→ Adversarial Training
● G mission: Fool D to missclassify.
● D mission: Discriminate between G samples and real samples.
11. The generator
Deterministic mapping from a latent random vector to sample from Pmodel (or
Pg), which should be similar to Pdata
E.g. DCGAN:
11
13. Adversarial Training (conceptual)
Generator
Real world
samples
Database
Discriminator
Real
Loss
Latentrandomvariable
Sample
Sample
Fake
13
z
D determines database images are Real,
whereas generated ones are Fake .
14. Adversarial Training
14
We have networks G and D, and training set with pdf Pdata. Notation:
● θ(G), θ(D) (Parameters of model G and D respectively)
● x ~ Pdata (M-dim sample from training data pdf)
● z ~ N(0, I) (sample from prior pdf, e.g. N-dim normal)
● G(z) = ẍ ~ Pmodel (M-dim sample from G network)
D network receives x or ẍ inputs → decides whether input is real or fake. It is optimized to learn: x is real
(1), ẍ is fake (0) (binary classifier).
G network maps sample z to G(z) = ẍ → it is optimized to maximize D mistakes.
NIPS 2016 Tutorial: Generative Adversarial Networks. Ian Goodfellow
15. Adversarial Training (batch update) (1)
● Pick a sample x from training set
● Show x to D and update weights to
output 1 (real)
16. Adversarial Training (batch update) (2)
● G maps sample z to ẍ
● show ẍ and update weights to output 0 (fake)
17. Adversarial Training (batch update) (3)
● Freeze D weights
● Update G weights to make D output 1 (just G weights!)
● Unfreeze D Weights and repeat
21. Training GANs dynamics
Iterate these two steps until convergence (which may not happen)
● Updating the discriminator should make it better at discriminating between real images and
generated ones (discriminator improves)
● Updating the generator makes it better at fooling the current discriminator (generator improves)
Eventually (we hope) that the generator gets so good that it is impossible for the discriminator to tell the
difference between real and generated images. Discriminator accuracy = 0.5
21
22. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
Key Idea: D is trained to detect fraud, (its parameters learn discriminative features
of “what is real/fake”). As backprop goes through D to G there happens to be
information leaking about the requirements for bank notes to look real. This makes
G perform small corrections to get closer and closer to what real samples would be.
23. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
Key Idea: D is trained to detect fraud, (its parameters learn discriminative features
of “what is real/fake”). As backprop goes through D to G there happens to be
information leaking about the requirements for bank notes to look real. This makes
G perform small corrections to get closer and closer to what real samples would be.
Caveat: this means GANs are not suitable for discrete tokens
predictions (e.g. words) → in that discrete space there is no
“small change” criteria to get to a neighbour (but can work in a
word embedding space for example).
24. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
100
100
FAKE: It’s
not even
green
25. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
100
100
FAKE:
There is no
watermark
26. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
100
100
FAKE:
Watermark
should be
rounded
27. Adversarial Training Analogy: is it fake money?
Imagine we have a counterfeiter (G) trying to make fake money, and the police (D)
has to detect whether money is real or fake.
After enough iterations, and if the counterfeiter is good enough (in terms of G
network it means “has enough parameters”), the police should be confused.
REAL?
FAKE?
28. Conditional GANs
GANs can be conditioned on other info extra to z:
text, labels, speech, etc..
z might capture random characteristics of the data
(variabilities of plausible futures), whilst c would
condition the deterministic parts !
For details on ways to condition GANs:
Ways of Conditioning Generative
Adversarial Networks (Wack et al.)
29. Conditional GANs
GANs can be conditioned on other info extra to z: text, labels, speech, etc..
z might capture random characteristics of the data (variabilities of plausible
futures), whilst c would condition the deterministic parts !
For details on ways to condition GANs:
Ways of Conditioning Generative
Adversarial Networks (Wack et al.)
30. Where is the downside...?
GANs are tricky and hard to train! We do not want to minimize a cost function.
Instead we want both networks to reach Nash equilibria (saddle point).
● Formulated as a “game” between two networks
● Unstable dynamics: hard to keep generator and discriminator in balance
● Optimization can oscillate between solutions
● Generator can collapse
Caveats
31. Where is the downside...?
GANs are tricky and hard to train! We do not want to minimize a cost function.
Instead we want both networks to reach Nash equilibria (saddle point).
Caveats
Because of extensive experience within the GAN community (with some
does-not-work-frustration from time to time), you can find some tricks and
tips on how to train a vanilla GAN here:
https://github.com/soumith/ganhacks
32. Generator collapse = parameter setting where it always emits sample sample.
● When collapse is imminent, gradient of D may point in similar directions for
many similar points → There is no coordination b/w D gradients as it
processes each sample independently in the minibatch.
Solving the collapse: Minibatch Discrimination
FAKE
FAKE
FAKE
FAKE
FAKE
DISCRIMINATORB
(Salimans et al. 2016)
33. Generator collapse = parameter setting where it always emits sample sample.
● When collapse is imminent, gradient of D may point in similar directions for
many similar points → There is no coordination b/w D gradients as it
processes each sample independently in the minibatch.
Solving the collapse: Minibatch Discrimination
FAKE
FAKE
FAKE
FAKE
FAKE
DISCRIMINATOR
How to tell G outputs to be
dissimilar if D makes B
independent decisions?
B
(Salimans et al. 2016)
34. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
L1
L2
L3
L4
Pick D features in
an intermediate
layer
(Salimans et al. 2016)
35. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
L1
L2
L3
L4
Pick D features in
an intermediate
layer
Multiply by
learnable tensor T
(Salimans et al. 2016)
36. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
L1
L2
L3
L4
Pick D features in
an intermediate
layer
Multiply by
learnable tensor T
Obtain Matrix M
(Salimans et al. 2016)
37. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
Introduce a notion of distance between rows of
interaction matrix M
(Salimans et al. 2016)
38. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
L1
L2
L3
L4
Minibatch Discrimination
,
concat
(Salimans et al. 2016)
39. Generator collapse = parameter setting where it always emits sample sample.
● Looking at multiple examples combined could potentially help avoiding
collapse of generator → model the closeness b/w examples in a minibatch
Solving the collapse: Minibatch Discrimination
L1
L2
L3
L4
Minibatch Discrimination
,
concat
Discriminator still classifies REAL/FAKE, but it has
side information to do so that tries to introduce a
notion of diversity in the batch.
(Salimans et al. 2016)
40. Batch Normalization is a technique to re-standardize every layer output distribution
to be N(0, I).
Virtual Batch Normalization
HiddenLayer
(Ioffe & Szegedy 2015)
We normalize each k dimension
with minibatch statistics
B
K features (hidden size)
41. Batch Normalization provokes intra-batch correlation, thus being G prone to mode
collapse → we can use a ref batch to smooth the statistics of our minibatch:
Virtual Batch Normalization
HiddenLayer
We normalize each k dimension
with minibatch statistics
2B
K features (hidden size)
reference
(Salimans et al. 2016)
42. Least Squares GAN
Main idea: shift to loss function that provides smooth & non-saturating gradients in D
● Because of sigmoid saturation in binary classification loss, G gets no info when
D gets to label true examples → vanishing gradients make G no learn
● Least squares loss improves learning with notion of distance of Pmodel to Pdata:
Least Squares Generative Adversarial
Networks, Mao et al. 2016
43. Other GAN implementations
Other GAN implementations try to stabilize learning dynamics, imposing new
divergences to be measured by D → gradients flow better and loss gets correlated
with generation quality:
● Wasserstein GAN (Arjovsky et al. 2017)
● BEGAN: Boundary Equilibrium Generative Adversarial Networks (Berthelot et al. 2017)
● Improved Training of Wasserstein GANs (Gulrajani et al. 2017)
44. GAN Applications
So far GANs have been extensively used in computer vision tasks:
● Generating images/generating video frames.
● Unsupervised feature extraction. Representation learning.
● Manipulating images (like a photoshop advanced level).
● Image coding/Super Resolution.
● Transferring image styles.
But now they’re extending to other fields, like speech!
● Speech Enhancement (Waveform)
● Unpaired Voice Conversion (Spectrum)
● Speech synthesis post-filtering (Spectrum)
45. Generating images/frames
(Radford et al. 2015)
Deep Conv. GAN (DCGAN) effectively generated 64x64 RGB images in a single
shot.
fully
connected Strided
transposed
conv layers
Conv layers,
no max-pool
fully
connected
46. Generating images/frames
(Radford et al. 2015)
Deep Conv. GAN (DCGAN) effectively generated 64x64 RGB images in a single
shot. For example bedrooms from LSUN dataset.
50. Unsupervised feature extraction/learning representations
Similarly to word2vec, GANs learn a distributed representation that disentangles
concepts such that we can perform semantic operations on the data manifold:
v(Man with glasses) - v(man) + v(woman) = v(woman with glasses)
(Radford et al. 2015)
51. Image super-resolution
Bicubic: not using data statistics. SRResNet: trained with MSE. SRGAN is able to
understand that there are multiple correct answers, rather than averaging.
(Ledig et al. 2016)
57. Conclusions
● Generative models are built to learn the underlying structures hidden in our high-dimensional
data.
● There are currently three main deep generative models: pixelCNN, VAE and GAN.
● PixelCNN factorizes an explicitly known discrete distribution with probability chain rule
○ They are the slowest generative models for their recursive nature.
● VAEs and GANs are capable of factorizing our highly-complex PDFs into a simpler prior of our
choice z.
● Once we trained VAEs with variational lower bound, we can generate new samples from our
learned z manifold.
● GANs use an implicit learning method to mimic our data distribution Pdata.
○ GANs are the sharpiest generators at the moment, and a very active research field.
○ They are the hardest ones to train though, because of their equilibria dynamics.