Jan 22, 2018 · I understand the basic structure of variational autoencoder and normal (deterministic) autoencoder and the math behind them, but when and why would I prefer one type of autoencoder to …

Jan 6, 2018 · 37 As per this and this answer, autoencoders seem to be a technique that uses neural networks for dimension reduction. I would like to additionally know what is a variational autoencoder …

Jul 10, 2023 · I am currently experiencing a suspected model collapse in a Variational Autoencoder (VAE) model I am working with. Below are details on the project setup and the issue at hand: Project …

Help Understanding Reconstruction Loss In Variational Autoencoder Ask Question Asked 7 years, 11 months ago Modified 5 years, 5 months ago

Jun 7, 2018 · The tensorflow tutorial for autoencoder uses R2-loss/MSE-loss for measuring the reconstruction loss. Where as the tensorflow tutorial for variational autoencoder uses binary cross …

Sep 17, 2021 · I think that the autoencoder (AE) generates the same new images every time we run the model because it maps the input image to a single point in the latent space. On the other hand, the …

Jun 8, 2021 · I'm training a Conv-VAE for MRI brain images (2D slices). the output of the model is sigmoid, and the loss function binary cross-entropy: x = input, x_hat = output rec_loss = nn.functional.

Mar 7, 2018 · How to weight KLD loss vs reconstruction loss in variational auto-encoder? Ask Question Asked 7 years, 9 months ago Modified 2 years, 3 months ago

May 21, 2018 · Variational autoencoder: Why reconstruction term is same to square loss? Ask Question Asked 7 years, 6 months ago Modified 6 years, 4 months ago

May 1, 2022 · I am currently dealing with variational autoencoders where I've read the original paper "An introduction to variational Bayes" from Kingma and Welling. I am currently still a little …