Questions

1. What happens if the learning rate of generator and discriminator models is high?
2. In a scenario where the generator and discriminator are very well trained, what is the probability of a given image being real?
3. Why do we use ConvTranspose2d in generating images?
4. Why do we have embeddings with a high embedding size than the number of classes in conditional GANs?
5. How can we generate images of men with beards?
6. Why do we have Tanh activation in the last layer in the generator and not ReLU or sigmoid?
7. Why did we get realistic images even though we did not denormalize the generated data?
8. What happens if we do not crop faces corresponding to images before training the GAN?
9. Why do the weights of the discriminator not get updated when the training generator is updated (as the generator_train_step function involves the discriminator network)?
10. Why do we fetch losses on both real and fake images while training the discriminator...