Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with variational-autoencoder
Search options not deleted
user 40011
For questions related to variational auto-encoders (VAEs). The first VAE was proposed in "Auto-Encoding Variational Bayes" (2013) by Diederik P. Kingma and Max Welling. There are several other VAEs, for example, the conditional VAE.
3
votes
0
answers
2k
views
Why VQ-VAE instead of VAE?
From the paper on VQ-VAE, it said that the vector quantized variational autoencoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network output discrete, rather than continuous, codecs
th …
- 185
3
votes
1
answer
541
views
Why don't we also need to approximate $p(x \mid z)$ in the VAE?
In the VAE, we approximate the probability distribution $p(z \mid x)$, where $z$ is the latent vector and $x$ is our data. The reason is that $p(z \mid x)$ becomes impossible to calculate for continuo …
- 185