Why do two autoencoders need to be trained? about faceswap-model

[coldstacks]

Thanks @torzdf. The reason I'm asking about this stuff is because I'm thinking of changing some of these things and seeing if it improves results or not. Since training these models takes significant computational power, I'm trying to understand why things are currently set up the way they are, so that I can tinker intelligently.

@Clorr are you the original developer who wrote the core Trainer.py and Model.py code mentioned above? It would be really helpful if you could tell me if any of the above choices were essentially arbitrary, vs if they were originally made for strong theoretical reasons (and/or because there was strong empirical evidence that it worked best). From that other issue, it sounds like for the first question (two encoders instead of just one), the answer is that two encoders empirically works best, in terms of giving you a model that's generic.

[torzdf]

I'm not sure if this will answer your question, but @Clorr gave an explanation here:
deepfakes/faceswap#229