- categories: ToDo
Problem with simple autoencoders - Representation state is not meaningful and not mapped to well to meaningful data after the decoding because of the irregularities
We want a nicely organized latent state
Base paper : auto encoders, auto variational bayes
ToDo : conditional distribution graph
2 distributions : data p(x) and latent p(z). We define two mappings p(z|x) and p(x|z).
We assume that p(z) is normal distribution, that allows us to estimate the likelihood of p(x|z), but we still miss p(z|x), we approximate it with gaussian with mi and sigma q(mu, sigma).
We can’t propagate through sampling phase, to do it we use a reparameterisation trick. It allows us to back propagation
Latent space is continious
Standard VAE produced blurry images of faces dataset because 9f the regularization term, other variants tried to improve it
Variants
- CVAE
- beta-VAE
- VQ-VAE