DynGAN: A Machine Learning Framework that Detects Collapsed Samples in the Generator by Thresholding on Observable Discriminator Outputs

Generative adversarial networks (GANs) are a popular tool for creating realistic data, but they often struggle with a problem called mode collapse. This happens when the variety of generated samples isn’t as diverse as real ones. Researchers have had trouble figuring out why this happens and finding a solution. A team of scientists from the […]

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