Transcription of Conditional Image Synthesis with Auxiliary Classifier GANs
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Conditional Image Synthesis with Auxiliary Classifier GANs
Conditional Image Synthesis with Auxiliary Classifier GANsAugustus Odena1 Christopher Olah1 Jonathon Shlens1 AbstractIn this paper we introduce new methods for theimproved training of generative adversarial net-works (GANs) for Image Synthesis . We con-struct a variant of GANs employing label condi-tioning that results in128 128resolution im-age samples exhibiting global coherence. Weexpand on previous work for Image quality as-sessment to provide two new analyses for assess-ing the discriminability and diversity of samplesfrom class- Conditional Image Synthesis analyses demonstrate that high resolutionsamples provide class information not present inlow resolution samples. Across 1000 ImageNetclasses,128 128samples are more than twiceas discriminable as artificially resized32 32samples. In addition, of the classes havesamples exhibiting diversity comparable to realImageNet IntroductionCharacterizing the structure of natural images has been arich research endeavor.
et al.,2016)). Autoregressive models dispense with latent variables and directly model the conditional distribution over pixels (van den Oord et al.,2016a;b). These models produce convincing samples but are costly to sample from and do not provide a latent representation. Invertible den-sity estimators transform latent variables directly using a
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