Hands-On Deep Learning Algorithms with Python

Architecture of the InfoGAN

Okay. What is really going on here? why are we doing this? To put it in simple terms, we split the input to the generator into two: z and c. Since both z and c are used for generating the image, they capture the semantic meaning of the image. The code c gives us the interpretable disentangled information about the image. So we try to find c given the generator output. However, we can't do this easily since we don't know the posterior, , so we use an auxiliary distribution, , to learn c.

This auxiliary distribution ...