Questions

1. What is an encoder in an autoencoder?
2. What loss function does an autoencoder optimize for?
3. How do autoencoders help in grouping similar images?
4. When is a convolutional autoencoder useful?
5. Why do we get non-intuitive images if we randomly sample from vector space of embeddings obtained from vanilla/convolutional autoencoders?
6. What are the loss functions that VAEs optimize for?
7. How do VAEs overcome the limitation of vanilla/convolutional autoencoders to generate new images?
8. During an adversarial attack, why do we modify the input image pixels and not the weight values?

9. In a neural style transfer, what are the losses that we optimize for?
10. Why do we consider the activation of different layers and not the original image when calculating style and content loss?
11. Why do we consider gram matrix loss and not the difference between images when calculating style loss?
12. Why do we warp images while building a model to generate deep fakes?