Hands-On Deep Learning Algorithms with Python

Summary

We started the chapter by learning about conditional GANs and how they can be used to generate our image of interest.

Later, we learned about InfoGANs, where the code c is inferred automatically based on the generated output, unlike CGAN, where we explicitly specify c. To infer c, we need to find the posterior, , which we don't have access to. So, we use an auxiliary distribution. We used mutual information to maximize the mutual information, , to maximize our knowledge about c given the generator output.

Then, we learned about CycleGANs, ...