Adversarial Feature Learning — mentioned by kastnerkyle

Fri, 03 Jun 2016 08:43:53 GMT

Yes, although the particular technique used here (variational autoencoder, or VAE) doesn't benefit from increasing the code space due to a particular penalty (KL divergence against a fixed N(0, 1) Gaussian prior) during training. So even by making it go to 1000 digits in the code space, the model will probably still only choose to use 5 or 10 dimensions unless the KL penalty is relaxed, making it closer to a standard autoencoder. An autoencoder with a much larger code space could be an improvement, or some newer works such as Adversarially Learned Inference / Adversarial Feature Learning [0…

Paper and discussion history. A mention is not an endorsement.

Adversarial Feature Learning · HackerPapers discussions

Adversarial Feature Learning — mentioned by kastnerkyle