Timezone: »
Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide range of different discriminator designs and training objectives. One popular design in earlier works is to include a classifier during training with the assumption that good classifiers can help eliminate samples generated with wrong classes. Nevertheless, including classifiers in cGANs often comes with a side effect of only generating easy-to-classify samples. Recently, some representative cGANs avoid the shortcoming and reach state-of-the-art performance without having classifiers. Somehow it remains unanswered whether the classifiers can be resurrected to design better cGANs. In this work, we demonstrate that classifiers can be properly leveraged to improve cGANs. We start by using the decomposition of the joint probability distribution to connect the goals of cGANs and classification as a unified framework. The framework, along with a classic energy model to parameterize distributions, justifies the use of classifiers for cGANs in a principled manner. It explains several popular cGAN variants, such as ACGAN, ProjGAN, and ContraGAN, as special cases with different levels of approximations, which provides a unified view and brings new insights to understanding cGANs. Experimental results demonstrate that the design inspired by the proposed framework outperforms state-of-the-art cGANs on multiple benchmark datasets, especially on the most challenging ImageNet. The code is available at https://github.com/sian-chen/PyTorch-ECGAN.
Author Information
Si-An Chen (National Taiwan University)
Chun-Liang Li (Google)
Hsuan-Tien Lin (National Taiwan University)
More from the Same Authors
-
2021 : Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration »
Si-An Chen · Chun-Liang Li · Hsuan-Tien Lin -
2021 : On the Role of Pre-training for Meta Few-Shot Learning »
Chia-You Chen · Hsuan-Tien Lin · Masashi Sugiyama · Gang Niu -
2021 Meetup: Taipei, Taiwan »
Hsuan-Tien Lin -
2021 : Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration »
Si-An Chen · Chun-Liang Li · Hsuan-Tien Lin -
2021 Poster: Robust Contrastive Learning Using Negative Samples with Diminished Semantics »
Songwei Ge · Shlok Mishra · Chun-Liang Li · Haohan Wang · David Jacobs -
2021 Poster: Object-aware Contrastive Learning for Debiased Scene Representation »
Sangwoo Mo · Hyunwoo Kang · Kihyuk Sohn · Chun-Liang Li · Jinwoo Shin -
2019 : Coffee Break & Poster Session 1 »
Yan Zhang · Jonathon Hare · Adam Prugel-Bennett · Po Leung · Patrick Flaherty · Pitchaya Wiratchotisatian · Alessandro Epasto · Silvio Lattanzi · Sergei Vassilvitskii · Morteza Zadimoghaddam · Theja Tulabandhula · Fabian Fuchs · Adam Kosiorek · Ingmar Posner · William Hang · Anna Goldie · Sujith Ravi · Azalia Mirhoseini · Yuwen Xiong · Mengye Ren · Renjie Liao · Raquel Urtasun · Haici Zhang · Michele Borassi · Shengda Luo · Andrew Trapp · Geoffroy Dubourg-Felonneau · Yasmeen Kussad · Christopher Bender · Manzil Zaheer · Junier Oliva · Michał Stypułkowski · Maciej Zieba · Austin Dill · Chun-Liang Li · Songwei Ge · Eunsu Kang · Oiwi Parker Jones · Kelvin Ka Wing Wong · Joshua Payne · Yang Li · Azade Nazi · Erkut Erdem · Aykut Erdem · Kevin O'Connor · Juan J Garcia · Maciej Zamorski · Jan Chorowski · Deeksha Sinha · Harry Clifford · John W Cassidy -
2019 : Posters »
Colin Graber · Yuan-Ting Hu · Tiantian Fang · Jessica Hamrick · Giorgio Giannone · John Co-Reyes · Boyang Deng · Eric Crawford · Andrea Dittadi · Peter Karkus · Matthew Dirks · Rakshit Trivedi · Sunny Raj · Javier Felip Leon · Harris Chan · Jan Chorowski · Jeff Orchard · Aleksandar Stanić · Adam Kortylewski · Ben Zinberg · Chenghui Zhou · Wei Sun · Vikash Mansinghka · Chun-Liang Li · Marco Cusumano-Towner -
2018 Poster: Nonparametric Density Estimation under Adversarial Losses »
Shashank Singh · Ananya Uppal · Boyue Li · Chun-Liang Li · Manzil Zaheer · Barnabas Poczos -
2018 Poster: REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis »
Yu-Shao Peng · Kai-Fu Tang · Hsuan-Tien Lin · Edward Chang -
2017 Poster: MMD GAN: Towards Deeper Understanding of Moment Matching Network »
Chun-Liang Li · Wei-Cheng Chang · Yu Cheng · Yiming Yang · Barnabas Poczos -
2012 Poster: Feature-aware Label Space Dimension Reduction for Multi-label Classification »
Yao-Nan Chen · Hsuan-Tien Lin -
2006 Poster: Ordinal Regression by Extended Binary Classification »
Ling Li · Hsuan-Tien Lin -
2006 Spotlight: Ordinal Regression by Extended Binary Classification »
Ling Li · Hsuan-Tien Lin