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.
information. One strategy is to supply both the generator and discriminator with class labels in order to produce class conditional samples (Mirza & Osindero,2014). Class con-ditional synthesis can significantly improve the quality of generated samples (van den Oord et al.,2016b). Richer side information such as image captions and bounding box lo-
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