Generative Adversarial Networks (GANs) are powerful tools for generating realistic data but face the challenge of mode collapse, leading to lack of diversity in outputs.
Mode collapse in GANs occurs when the generator is constrained to producing a limited set of outputs, failing to capture the full range of the training data.
Catastrophic forgetting can further exacerbate mode collapse in GANs, causing the generator to lose previously learned representations when learning new tasks.
Discriminator overfitting adds to the problem, as it can lead to a vanishing generator loss, preventing the generator from effectively improving its outputs.
#generative-adversarial-networks #machine-learning #mode-collapse #catastrophic-forgetting #deep-learning
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