What are the limitations of GAN architecture?

What are the limitations of GAN architecture?

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 causes training instability in GANs?

Training instability in GANs can be caused by the adversarial nature of the model, where the generator and discriminator can become unbalanced, leading to oscillations or divergence during training.

How can these limitations be addressed?

To address these limitations, techniques like using different architectures, adding regularization, or employing alternative training strategies can be helpful.

Are there any specific applications where GANs are particularly effective despite these limitations?

Yes, GANs are particularly effective in applications like image generation, style transfer, and data augmentation, where their strengths can outweigh the limitations.