Timezone: »
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion. While they were successfully applied to many problems, training a GAN is a notoriously challenging task and requires a significant number of hyperparameter tuning, neural architecture engineering, and a non-trivial amount of ``tricks". The success in many practical applications coupled with the lack of a measure to quantify the failure modes of GANs resulted in a plethora of proposed losses, regularization and normalization schemes, as well as neural architectures. In this work we take a sober view of the current state of GANs from a practical perspective. We discuss and evaluate common pitfalls and reproducibility issues, open-source our code on Github, and provide pre-trained models on TensorFlow Hub.
Author Information
Karol Kurach (Google Brain)
Mario Lucic (Google Brain)
Xiaohua Zhai (Google Brain)
Marcin Michalski (Google Brain)
Sylvain Gelly (Google Brain)
Related Events (a corresponding poster, oral, or spotlight)
-
2019 Oral: A Large-Scale Study on Regularization and Normalization in GANs »
Wed. Jun 12th 07:10 -- 07:15 PM Room Hall A
More from the Same Authors
-
2022 : SI-Score »
Jessica Yung · Rob Romijnders · Alexander Kolesnikov · Lucas Beyer · Josip Djolonga · Neil Houlsby · Sylvain Gelly · Mario Lucic · Xiaohua Zhai -
2023 Poster: Scaling Vision Transformers to 22 Billion Parameters »
Mostafa Dehghani · Josip Djolonga · Basil Mustafa · Piotr Padlewski · Jonathan Heek · Justin Gilmer · Andreas Steiner · Mathilde Caron · Robert Geirhos · Ibrahim Alabdulmohsin · Rodolphe Jenatton · Lucas Beyer · Michael Tschannen · Anurag Arnab · Xiao Wang · Carlos Riquelme · Matthias Minderer · Joan Puigcerver · Utku Evci · Manoj Kumar · Sjoerd van Steenkiste · Gamaleldin Elsayed · Aravindh Mahendran · Fisher Yu · Avital Oliver · Fantine Huot · Jasmijn Bastings · Mark Collier · Alexey Gritsenko · Vighnesh N Birodkar · Cristina Vasconcelos · Yi Tay · Thomas Mensink · Alexander Kolesnikov · Filip Pavetic · Dustin Tran · Thomas Kipf · Mario Lucic · Xiaohua Zhai · Daniel Keysers · Jeremiah Harmsen · Neil Houlsby -
2023 Poster: Tuning Computer Vision Models With Task Rewards »
André Susano Pinto · Alexander Kolesnikov · Yuge Shi · Lucas Beyer · Xiaohua Zhai -
2023 Oral: Scaling Vision Transformers to 22 Billion Parameters »
Mostafa Dehghani · Josip Djolonga · Basil Mustafa · Piotr Padlewski · Jonathan Heek · Justin Gilmer · Andreas Steiner · Mathilde Caron · Robert Geirhos · Ibrahim Alabdulmohsin · Rodolphe Jenatton · Lucas Beyer · Michael Tschannen · Anurag Arnab · Xiao Wang · Carlos Riquelme · Matthias Minderer · Joan Puigcerver · Utku Evci · Manoj Kumar · Sjoerd van Steenkiste · Gamaleldin Elsayed · Aravindh Mahendran · Fisher Yu · Avital Oliver · Fantine Huot · Jasmijn Bastings · Mark Collier · Alexey Gritsenko · Vighnesh N Birodkar · Cristina Vasconcelos · Yi Tay · Thomas Mensink · Alexander Kolesnikov · Filip Pavetic · Dustin Tran · Thomas Kipf · Mario Lucic · Xiaohua Zhai · Daniel Keysers · Jeremiah Harmsen · Neil Houlsby -
2023 Poster: Underspecification Presents Challenges for Credibility in Modern Machine Learning »
Alexander D'Amour · Katherine Heller · Dan Moldovan · Ben Adlam · Babak Alipanahi · Alex Beutel · Christina Chen · Jonathan Deaton · Jacob Eisenstein · Matthew Hoffman · Farhad Hormozdiari · Neil Houlsby · Shaobo Hou · Ghassen Jerfel · Alan Karthikesalingam · Mario Lucic · Yian Ma · Cory McLean · Diana Mincu · Akinori Mitani · Andrea Montanari · Zachary Nado · Vivek Natarajan · Christopher Nielson · Thomas F. Osborne · Rajiv Raman · Kim Ramasamy · Rory sayres · Jessica Schrouff · Martin Seneviratne · Shannon Sequeira · Harini Suresh · Victor Veitch · Maksym Vladymyrov · Xuezhi Wang · Kellie Webster · Steve Yadlowsky · Taedong Yun · Xiaohua Zhai · D. Sculley -
2022 : SI-Score »
Jessica Yung · Rob Romijnders · Alexander Kolesnikov · Lucas Beyer · Josip Djolonga · Neil Houlsby · Sylvain Gelly · Mario Lucic · Xiaohua Zhai -
2019 Poster: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Poster: Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities »
Octavian-Eugen Ganea · Sylvain Gelly · Gary Becigneul · Aliaksei Severyn -
2019 Oral: Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities »
Octavian-Eugen Ganea · Sylvain Gelly · Gary Becigneul · Aliaksei Severyn -
2019 Oral: Parameter-Efficient Transfer Learning for NLP »
Neil Houlsby · Andrei Giurgiu · Stanislaw Jastrzebski · Bruna Morrone · Quentin de Laroussilhe · Andrea Gesmundo · Mona Attariyan · Sylvain Gelly -
2019 Poster: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2019 Poster: High-Fidelity Image Generation With Fewer Labels »
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly -
2019 Oral: High-Fidelity Image Generation With Fewer Labels »
Mario Lucic · Michael Tschannen · Marvin Ritter · Xiaohua Zhai · Olivier Bachem · Sylvain Gelly -
2019 Oral: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations »
Francesco Locatello · Stefan Bauer · Mario Lucic · Gunnar Ratsch · Sylvain Gelly · Bernhard Schölkopf · Olivier Bachem -
2017 Poster: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause -
2017 Poster: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Talk: Uniform Deviation Bounds for k-Means Clustering »
Olivier Bachem · Mario Lucic · Hamed Hassani · Andreas Krause -
2017 Talk: Distributed and Provably Good Seedings for k-Means in Constant Rounds »
Olivier Bachem · Mario Lucic · Andreas Krause