What does "variational" mean? - Cross Validated
Apr 17, 2018 · Does the use of "variational" always refer to optimization via variational inference? Examples: "Variational auto-encoder" "Variational Bayesian methods" "Variational …
deep learning - When should I use a variational autoencoder as …
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 …
bayesian - What are variational autoencoders and to what learning …
Jan 6, 2018 · Even though variational autoencoders (VAEs) are easy to implement and train, explaining them is not simple at all, because they blend concepts from Deep Learning and …
Understanding the Evidence Lower Bound (ELBO) - Cross Validated
Jun 24, 2022 · I am reading this tutorial about Variational Inference, which includes the following depiction of ELBO as the lower bound on log-likelihood on the third page. In the tutorial, $x_i$ …
regression - What is the difference between Variational Inference …
Jul 13, 2022 · I have been reading about variational inference and it is relation to Bayesian regression. It seems there are two versions The first version is discussed here. The second …
How to Resolve Variational Autoencoder (VAE) Model Collapse in ...
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 …
Normalizing flows as a generalization of variational autoencoders ...
Apr 24, 2021 · For those curious to link the said techniques to more state-of-the-art generative algorithms, diffusion models can be transformed into continuous normalizing flows (CNFs) and …
autoencoders - Exploring vae latent space - Cross Validated
Jul 16, 2024 · The SDs of the inferred variational beliefs can indeed be very small, if the network is simply very certain about the values of the latents given the input. For instance, it may be …
kullback leibler - How should I intuitively understand the KL ...
How should I intuitively understand the KL divergence loss in variational autoencoders? [duplicate] Ask Question Asked 6 years, 10 months ago Modified 6 years, 2 months ago
Loss function autoencoder vs variational-autoencoder or MSE-loss …
Jun 7, 2018 · Where as the tensorflow tutorial for variational autoencoder uses binary cross-entropy for measuring the reconstruction loss. Can some please tell me WHY, based on the …