Chapter 10 - Reconstructing Inputs Using Autoencoders

1. Autoencoders are unsupervised learning algorithms. Unlike other algorithms, autoencoders learn to reconstruct the input, that is, an autoencoder takes the input and learns to reproduce the input as an output.

2. We can define our loss function as a difference between the actual input and reconstructed input as follows:

   

   Here, is the number of training samples.

3. Convolutional Autoencoder (CAE) that uses a convolutional network instead of a vanilla neural network. In the vanilla autoencoders, encoders and decoders are basically a feedforward network. But in CAEs, they are basically convolutional networks. This means the encoder consists of convolutional layers and the decoder consists of transposed convolutional layers, instead of a raw feedforward network.
4. Denoising Autoencoders (DAE) are another small variant of the autoencoder...