What are the limitations of GANs?

What are the main limitations of GANs?

GANs, or Generative Adversarial Networks, have several limitations, including mode collapse, training instability, and difficulty in evaluating the quality of generated samples.

Can you explain mode collapse?

Mode collapse occurs when the generator produces a limited variety of outputs, essentially focusing on a few modes of the data distribution instead of capturing the full diversity.

What about training instability?

Training instability in GANs can lead to oscillations or divergence, making it hard to find a balance between the generator and discriminator during training.

How can these limitations be addressed?

Researchers have proposed various techniques, such as using different architectures, regularization methods, and improved training algorithms to mitigate these limitations.