Disentangled VAE

Pytorch implementation of VAEs for unsupervised disentangled representation learning.

Plans

• Models:
  • VAE
  • BetaVAE
  • AnnealVAE
  • DIP-VAE
  • beta-TCVAE
  • FactorVAE
• Metrics:
  • BetaVAE Scores
  • FactorVAE Scores
  • MIG
  • SAP

This codes only supports dsprites dataset right now. And more training results will be released recently.

Besides, I found it hard to get an impressive disentangling results, which means that it's not so easy to reimplement the results in paper. I think it may be due to the well-known unidentifiablity problem of this kind of unsupervised representation methods. Here are things I found useful in experiment:

• annealing methods is worth trying, it's more easier to train than BetaVAE.
• binary_cross_entropy is useful for dsprites.

Training Results

AnnealVAE	BetaVAE scores
	0.8114333371321361

The model learned some factors( size, shape, y-shift, x-shift in 1st, 5th, 6th, 9th components ), but did not exhibit well-disentangled behaviour.