Conditional Image Synthesis with Auxiliary Classifier GANs
Conditional Image Synthesis with Auxiliary Classifier GANsAugustus Odena1Christopher Olah1Jonathon Shlens1AbstractIn 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.
ible (Dinh et al.,2016). This technique allows for exact log-likelihood computation and exact inference, but the in-vertibility constraint is restrictive. Generative adversarial networks (GANs) offer a distinct and promising approach that focuses on a game-theoretic formulation for training an image synthesis model (Good-fellow et al.,2014).
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