Hi r/MachineLearning,

With some friends, we replicated some of the most important disentangled VAE (beta-TCVAE, FAactorVAE, both versions of beta-VAE). The motivations were the following:

• Have a modular framework easily extendable for any type of VAE
• Separate the loss and the architecture to have a better understanding of what causes the improvements in new papers (many papers change both and it’s thus hard to disentangle – no puns intended – where the improvements come from).
• Have a clean and easily extendable visualisation pipeline for VAEs
• Replicate quantitative metrics to compare disentanglement

I hope you find it useful, and please let me know if there is anything you think would be useful to add (new disentangle VAE loss, add GAN for reconstruction loss, …)

https://github.com/YannDubs/disentangling-vae

PS: it’s my first post on reddit, please let me know if I’m doing something wrong.