Portrait de Jean Pouget-Abadie n'est pas disponible

Jean Pouget-Abadie

Alumni

Publications

Generative Adversarial Networks
Ian G Goodfellow
Jean Pouget-Abadie
Mehdi Mirza
Bing Xu
David Warde-Farley
Sherjil Ozair
Aaron Courville
Yoshua Bengio
Generative Adversarial Networks (GANs) are a type of deep learning techniques that have shown remarkable success in generating realistic ima… (voir plus)ges, videos, and other types of data. This paper provides a comprehensive guide to GANs, covering their architecture, loss functions, training methods, applications, evaluation metrics, challenges, and future directions. We begin with an introduction to GANs and their historical development, followed by a review of the background and related work. We then provide a detailed overview of the GAN architecture, including the generator and discriminator networks, and discuss the key design choices and variations. Next, we review the loss functions utilized in GANs, including the original minimax objective, as well as more recent approaches s.a. Wasserstein distance and gradient penalty. We then delve into the training of GANs, discussing common techniques s.a. alternating optimization, minibatch discrimination, and spectral normalization. We also provide a survey of the various applications of GANs across domains. In addition, we review the evaluation metrics utilized to assess the diversity and quality of GAN-produced data. Furthermore, we discuss the challenges and open issues in GANs, including mode collapse, training instability, and ethical considerations. Finally, we provide a glimpse into the future directions of GAN research, including improving scalability, developing new architectures, incorporating domain knowledge, and exploring new applications. Overall, this paper serves as a comprehensive guide to GANs, providing both theoretical and practical insights for researchers and practitioners in the field.
2021-12-15
ArXiv (prépublication)
doi.org
arxiv.org
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension
Oliver Bender
Franz Josef Och
Yoshua Bengio
R´ejean Ducharme
Pascal Vincent
Kevin Clark
Quoc Minh-Thang Luong
V. Le
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Adam Fisch
Alon Talmor
Robin Jia
Minjoon Seo
Michael R. Glass
A. Gliozzo
Rishav Chakravarti
Ian J Goodfellow
Jean Pouget-Abadie … (voir 39 de plus)
Mehdi Mirza
Serhii Havrylov
Ivan Titov. 2017
Emergence
Jun-Tao He
Jiatao Gu
Jiajun Shen
Marc’Aurelio
Matthew Henderson
I. Casanueva
Nikola Mrkˇsi´c
Pei-hao Su
Tsung-Hsien Wen
Ivan Vuli´c
Yikang Shen
Yi Tay
Che Zheng
Dara Bahri
Donald
Metzler Aaron
Courville
Structformer
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Thomas Wolf
Lysandre Debut
Julien Victor Sanh
Clement Chaumond
Anthony Delangue
Pier-339 Moi
Tim ric Cistac
R´emi Rault
Morgan Louf
Qizhe Xie
Eduard H. Hovy
Silei Xu
Sina Jandaghi Semnani
Giovanni Campagna
Pretrained language models have significantly 001 improved the performance of down-stream 002 language understanding tasks, including ex-00… (voir plus)3 tractive question answering, by providing 004 high-quality contextualized word embeddings. 005 However, training question answering models 006 still requires large amounts of annotated data 007 for specific domains. In this work, we pro-008 pose a cooperative, self-play learning frame-009 work, REGEX, for automatically generating 010 more non-trivial question-answer pairs to im-011 prove model performance. REGEX is built 012 upon a masked answer extraction task with an 013 interactive learning environment containing an 014 answer entity REcognizer, a question Gener-015 ator, and an answer EXtractor. Given a pas-016 sage with a masked entity, the generator gen-017 erates a question around the entity, and the 018 extractor is trained to extract the masked en-019 tity with the generated question and raw texts. 020 The framework allows the training of question 021 generation and answering models on any text 022 corpora without annotation. We further lever-023 age a reinforcement learning technique to re-024 ward generating high-quality questions and to 025 improve the answer extraction model’s perfor-026 mance. Experiment results show that REGEX 027 outperforms the state-of-the-art (SOTA) pre-028 trained language models and transfer learning 029 approaches on standard question-answering 030 benchmarks, and yields the new SOTA per-031 formance under given model size and transfer 032 learning settings. 033
Generative adversarial networks
Moez Krichen
Ian G Goodfellow
Jean Pouget-Abadie
Mehdi Mirza
Bing Xu
David Warde-Farley
Sherjil Ozair
Aaron Courville
Yoshua Bengio
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal … (voir plus)of a generative model is to study a collection of training examples and learn the probability distribution that generated them. Generative Adversarial Networks (GANs) are then able to generate more examples from the estimated probability distribution. Generative models based on deep learning are common, but GANs are among the most successful generative models (especially in terms of their ability to generate realistic high-resolution images). GANs have been successfully applied to a wide variety of tasks (mostly in research settings) but continue to present unique challenges and research opportunities because they are based on game theory while most other approaches to generative modeling are based on optimization.
2014-06-10
Communications of the ACM (publié)
doi.org
arxiv.org