What are the differences between GANs and other generative models?

What are the main differences between GANs and other generative models?

GANs, or Generative Adversarial Networks, use a two-part system of a generator and a discriminator, while other models like VAEs use a single encoder-decoder structure.

Can you explain how the training process differs between GANs and VAEs?

Sure! GANs train by having the generator create data to fool the discriminator, while VAEs optimize a loss function that includes reconstruction and regularization terms.

Which model is generally better for image generation?

GANs are often preferred for high-quality image generation due to their ability to produce sharper images, but VAEs can be more stable during training.

Are there any specific applications where one model outperforms the other?

Yes, GANs excel in tasks like image-to-image translation and super-resolution, while VAEs are great for tasks requiring latent space manipulation, like interpolation.