Chapter 12 - Image Generation Using GANs

1. What happens if the learning rate of generator and discriminator models is high?
   Empirically, it is observed that the model stability is lower.
2. In a scenario where the generator and discriminator are very well trained, what is the probability of a given image being real?
   0.5.
3. Why do we use convtranspose2d in generating images?
   We cannot upscale/ generate images using a linear layer.
4. Why do we have embeddings with high embedding size than the number of classes in Conditional GANs?
   Using more parameters gives the model more degrees of freedom to learn the important features of each class.
5. How can we generate images of men that have a beard?
   By using a conditional GAN. Just like we had male and female images, we can have bearded males and other such classes while training model.
6. Why do we have Tanh activation at the last layer in the generator and not ReLU or Sigmoid?
   The pixel range of normalized images is [-1,1] and hence we use Tanh
7. Why did we...