What is mode collapse in the context of GANs?

In the context of Generative Adversarial Networks (GANs), mode collapse is specifically referred to as a situation where the Generator produces a limited diversity of outputs. This occurs when the Generator learns to produce only a few types of outputs that consistently fool the Discriminator, rather than generating a wide range of diverse samples that accurately represent the training data distribution.

Mode collapse can be problematic as it means that the Generator is not fully exploring the potential variability within the training data. Ideally, a well-functioning Generator should be able to produce a diverse set of outputs that reflect the various modes present in the input data distribution. When mode collapse happens, it can lead to a lack of variety in the generated samples, which undermines the purpose of the GAN, as it is supposed to model the full diversity of the target distribution. Addressing mode collapse often involves techniques aimed at encouraging the Generator to explore different areas of the output space, thus promoting a richer set of generated outputs.