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This paper proposes a series of new approaches to improve GenerativeAdversarial Network (GAN) for conditional image synthesis and we name theproposed model as ArtGAN. One of the key innovation of ArtGAN is that, thegradient of the loss function w.r.t. the label (randomly assigned to eachgenerated image) is back-propagated from the categorical discriminator to thegenerator. With the feedback from the label information, the generator is ableto learn more efficiently and generate image with better quality. Inspired byrecent works, an autoencoder is incorporated into the categorical discriminatorfor additional complementary information. Last but not least, we introduce anovel strategy to improve the image quality. In the experiments, we evaluateArtGAN on CIFAR-10 and STL-10 via ablation studies. The empirical resultsshowed that our proposed model outperforms the state-of-the-art results onCIFAR-10 in terms of Inception score. Qualitatively, we demonstrate that ArtGANis able to generate plausible-looking images on Oxford-102 and CUB-200, as wellas able to draw realistic artworks based on style, artist, and genre. Thesource code and models are available at: https://github.com/cs-chan/ArtGAN

Improved ArtGAN for Conditional Synthesis of Natural Image and Artwork