New framework may solve mode collapse in generative adversarial network

Generative adversarial networks (GANs) are widely used to synthesize intricate and realistic data by learning the distribution of authentic real samples. However, a significant challenge that GANs face is mode collapse, where the diversity of generated samples is notably lower than that of real samples. The complexity of GANs and their training process has made it difficult to reveal the underlying mechanism of mode collapse.

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