Generating Pusheen with AI

The BEGAN model uses a loss equation based on the Wasserstein distance, except its goal is to minimize the absolute value of the autoencoder losses on the real and fake images instead of the images themselves. In practice it drops the absolute value and minimizes the reconstruction loss on real images minus the reconstruction loss on fake images. Additionally, it introduces a weighting term on the fake reconstruction loss which changes proportaional to the difference between the fake and real reconstruction losses; this serves to maintain a balance between the discriminator and generator so one does not easily win over the other. I'll be releasing the code soon but to summarize, the architectures and hyperparameters that typically worked well with for the discriminator and generator were: I did a fair amount of hyperparameter searching to get a model that worked (keep scrolling for some failures) but I think it ended up being a pretty decent cat generator. Below is a training video of one of the models that I use in the demos, where every 250 steps I take 16 samples from the generator.