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Stylized Adversarial AutoEncoder for Image Generation
Proceedings of the 2017 ACM on Multimedia Conference - MM '17
In this paper, we propose an autoencoder-based generative adversarial network (GAN) for automatic image generation, which is called "stylized adversarial autoencoder". Di erent from existing generative autoencoders which typically impose a prior distribution over the latent vector, the proposed approach splits the latent variable into two components: style feature and content feature, both encoded from real images. The split of the latent vector enables us adjusting the content and the style ofdoi:10.1145/3123266.3123450 dblp:conf/mm/ZhaoDHLH17 fatcat:hnmoizwspnbanis3p3ncfhcrq4