Structure-preserving GANs

Jeremiah Birrell, Markos Katsoulakis, Luc Rey-Bellet, Wei Zhu
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:1982-2020, 2022.

Abstract

Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions. We introduce structure-preserving GANs as a data-efficient framework for learning distributions with additional structure such as group symmetry, by developing new variational representations for divergences. Our theory shows that we can reduce the discriminator space to its projection on the invariant discriminator space, using the conditional expectation with respect to the sigma-algebra associated to the underlying structure. In addition, we prove that the discriminator space reduction must be accompanied by a careful design of structured generators, as flawed designs may easily lead to a catastrophic “mode collapse” of the learned distribution. We contextualize our framework by building symmetry-preserving GANs for distributions with intrinsic group symmetry, and demonstrate that both players, namely the equivariant generator and invariant discriminator, play important but distinct roles in the learning process. Empirical experiments and ablation studies across a broad range of data sets, including real-world medical imaging, validate our theory, and show our proposed methods achieve significantly improved sample fidelity and diversity—almost an order of magnitude measured in Frechet Inception Distance—especially in the small data regime.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-birrell22a, title = {Structure-preserving {GAN}s}, author = {Birrell, Jeremiah and Katsoulakis, Markos and Rey-Bellet, Luc and Zhu, Wei}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {1982--2020}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/birrell22a/birrell22a.pdf}, url = {https://proceedings.mlr.press/v162/birrell22a.html}, abstract = {Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions. We introduce structure-preserving GANs as a data-efficient framework for learning distributions with additional structure such as group symmetry, by developing new variational representations for divergences. Our theory shows that we can reduce the discriminator space to its projection on the invariant discriminator space, using the conditional expectation with respect to the sigma-algebra associated to the underlying structure. In addition, we prove that the discriminator space reduction must be accompanied by a careful design of structured generators, as flawed designs may easily lead to a catastrophic “mode collapse” of the learned distribution. We contextualize our framework by building symmetry-preserving GANs for distributions with intrinsic group symmetry, and demonstrate that both players, namely the equivariant generator and invariant discriminator, play important but distinct roles in the learning process. Empirical experiments and ablation studies across a broad range of data sets, including real-world medical imaging, validate our theory, and show our proposed methods achieve significantly improved sample fidelity and diversity—almost an order of magnitude measured in Frechet Inception Distance—especially in the small data regime.} }
Endnote
%0 Conference Paper %T Structure-preserving GANs %A Jeremiah Birrell %A Markos Katsoulakis %A Luc Rey-Bellet %A Wei Zhu %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-birrell22a %I PMLR %P 1982--2020 %U https://proceedings.mlr.press/v162/birrell22a.html %V 162 %X Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions. We introduce structure-preserving GANs as a data-efficient framework for learning distributions with additional structure such as group symmetry, by developing new variational representations for divergences. Our theory shows that we can reduce the discriminator space to its projection on the invariant discriminator space, using the conditional expectation with respect to the sigma-algebra associated to the underlying structure. In addition, we prove that the discriminator space reduction must be accompanied by a careful design of structured generators, as flawed designs may easily lead to a catastrophic “mode collapse” of the learned distribution. We contextualize our framework by building symmetry-preserving GANs for distributions with intrinsic group symmetry, and demonstrate that both players, namely the equivariant generator and invariant discriminator, play important but distinct roles in the learning process. Empirical experiments and ablation studies across a broad range of data sets, including real-world medical imaging, validate our theory, and show our proposed methods achieve significantly improved sample fidelity and diversity—almost an order of magnitude measured in Frechet Inception Distance—especially in the small data regime.
APA
Birrell, J., Katsoulakis, M., Rey-Bellet, L. & Zhu, W.. (2022). Structure-preserving GANs. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:1982-2020 Available from https://proceedings.mlr.press/v162/birrell22a.html.

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