Chapter 11 - Autoencoders and Image Manipulation

1. What is an encoder in autoencoder?
   A smaller neural network that converts an image into a vector representation.
2. What loss function does autoencoder optimize for?
   Pixel level mean square error, directly comparing prediction with input.
3. How do autoencoders help in grouping similar images?
   Similar images will return similar encodings, which are easier to cluster.
4. When is the Convolutional autoencoder useful?
   When the inputs are images.

5. Why do we get non-intuitive images if we randomly sample from vector space of embeddings obtained from vanilla/convolutional autoencoder?
   The range of values in encodings is unconstrained, so proper outputs are highly dependent on the right range of values. Random sampling, in general, assumes a 0 mean and 1 standard deviation.
6. What are the loss functions that the Variational autoencoder optimizes for?
   Pixel level MSE and KL-Divergence of the distribution of mean and standard deviation from the encoder.
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