CMU’s Latest Machine Learning Research Analyzes and Improves Spectral Normalization In GANs

GANs (generative adversarial networks) are cutting-edge deep generative models that are best known for producing high-resolution, photorealistic photographs. The goal of GANs is to generate random samples from a target data distribution with only a small set of training examples available. This is accomplished by learning two functions: a generator G that maps random input noise to a generated sample, and a discriminator D that attempts to categorize input samples as accurate (i.e., from the training dataset) or fake (i.e., …

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