Conditional Image Synthesis with Auxiliary Classifier GANs

Augustus Odena, Christopher Olah, Jonathon Shlens
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2642-2651, 2017.

Abstract

In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in 128×128 resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, 128×128 samples are more than twice as discriminable as artificially resized 32×32 samples. In addition, 84.7\% of the classes have samples exhibiting diversity comparable to real ImageNet data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-odena17a, title = {Conditional Image Synthesis with Auxiliary Classifier {GAN}s}, author = {Augustus Odena and Christopher Olah and Jonathon Shlens}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {2642--2651}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/odena17a/odena17a.pdf}, url = {https://proceedings.mlr.press/v70/odena17a.html}, abstract = {In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in $128\times 128$ resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, $128\times 128$ samples are more than twice as discriminable as artificially resized $32\times 32$ samples. In addition, 84.7\% of the classes have samples exhibiting diversity comparable to real ImageNet data.} }
Endnote
%0 Conference Paper %T Conditional Image Synthesis with Auxiliary Classifier GANs %A Augustus Odena %A Christopher Olah %A Jonathon Shlens %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-odena17a %I PMLR %P 2642--2651 %U https://proceedings.mlr.press/v70/odena17a.html %V 70 %X In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in $128\times 128$ resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, $128\times 128$ samples are more than twice as discriminable as artificially resized $32\times 32$ samples. In addition, 84.7\% of the classes have samples exhibiting diversity comparable to real ImageNet data.
APA
Odena, A., Olah, C. & Shlens, J.. (2017). Conditional Image Synthesis with Auxiliary Classifier GANs. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:2642-2651 Available from https://proceedings.mlr.press/v70/odena17a.html.

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