Adversarial Learning with Local Coordinate Coding

ICML 2018 Jiezhang CaoYong GuoQingyao WuChunhua ShenJunzhou HuangMingkui Tan

Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data... (read more)

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