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

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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., not from the training dataset) (i.e., produced by the generator). Despite its success in enhancing the sample quality of data-driven generative models, GANs' adversarial training adds to instability. Small changes in hyperparameters, as well as randomness in the optimization process, might cause training to fail.

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