On the Effects of Batch and Weight Normalization in Generative Adversarial Networks

Sitao Xiang, Hao Li

Generative adversarial networks (GANs) are highly effective unsupervised learning frameworks that can generate very sharp data, even for data such as images with complex, highly mu…