As we mentioned in the previous chapter, the Generator and Discriminator consist of a Deconvolutional Network (DNN: https://www.quora.com/How-does-a-deconvolutional-neural-network-work) and Convolutional Neural Network (CNN: http://cs231n.github.io/convolutional-networks/):
- CNN is a a type of neural network that encodes hundreds of pixels of an image into a vector of small dimensions (z), which is a summary of the image
- DNN is a network that learns some filters to recover the original image from z
Also, the discriminator will output one or zero to indicate whether the input image is from the actual dataset or generated by the generator. On the other side, the generator will try to replicate images similar to the original dataset based on the latent space z, which might follow a Gaussian distribution. So, the goal of the discriminator is to correctly discriminate...
