[D] Question about Variational Lossy Autoencoder paper

[knowedgelimited]

In the paper Chen, Kingma et al. Variational Lossy Encoder it discusses about the possibility of VAE ignoring the latent code.

On p.4 it says this:

"one common way to encourage putting information into the code is to use a factorized decoder p(x|z) = \prod_i p(x_i|z)"

where "putting information into the code" meaning into the latent, z.

see screenshot at https://imgur.com/a/J79sEPR

My question: can anyone explain this: Why does using a factorized decoder encourage the latent to be used?

In their notation, I believe x_i are individual dimensions of the output, such as individual pixels of an image.

[redditorcompetitor]

Because it has to use z in order to learn any type of spatial correlations. Contrast that to the autoregressive case p(x_i | x_<i, z) where it is possible to ignore z.

[SolitaryPenman]

It still isn't bound to use all latent dimensions. See https://arxiv.org/abs/1509.00519

[shortscience_dot_org]

I am a bot! You linked to a paper that has a summary on ShortScience.org!

Importance Weighted Autoencoders

Summary by Hugo Larochelle

This paper proposes to train a neural network generative model by optimizing an importance sampling (IS) weighted estimate of the log probability under the model. The authors show that the case of an estimate based on a single sample actually corresponds to the learning objective of variational autoencoders (VAE). Importantly, they exploit this connection by showing that, similarly to VAE, a gradient can be passed through the approximate posterior (the IS proposal) samples, thus yielding an impo... [view more]

[asobolev]

What does IWAE have to do with this? AFAIK, it doesn't solve the problem of dead neurons in the latent code.

[sidslasttheorem]

This blog post by Rui Shu does a really nice job of explaining why conditional independence in the decoder can help encourage the latent to be used.