Methods › Computer Vision › Generative Models › NVAE
Nouveau VAE
NVAE
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
NVAE, or Nouveau VAE, is deep, hierarchical variational autoencoder. It can be trained with the original VAE objective, unlike alternatives such as VQ-VAE-2. NVAE’s design focuses on tackling two main challenges: (i) designing expressive neural networks specifically for VAEs, and (ii) scaling up the training to a large number of hierarchical groups and image sizes while maintaining training stability.
To tackle long-range correlations in the data, the model employs hierarchical multi-scale modelling. The generative model starts from a small spatially arranged latent variables as 𝐳₁ and samples from the hierarchy group-by-group while gradually doubling the spatial dimensions. This multi-scale approach enables NVAE to capture global long-range correlations at the top of the hierarchy and local fine-grained dependencies at the lower groups.
Additional design choices include the use of residual cells for the generative models and the encoder, which employ a number of tricks and modules to achieve good performance, and the use of residual normal distributions to smooth optimization. See the components section for more details.
Papers archive 2025-07-28
5 shown of 5, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck 27 Jul 2022 · 0 repositories · arXiv:2207.13529
-
Alleviating Adversarial Attacks on Variational Autoencoders with MCMC 18 Mar 2022 · 1 repository · arXiv:2203.09940
-
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values 3 Mar 2022 · 1 repository · arXiv:2203.01993
-
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection 8 Jan 2021 · 0 repositories · arXiv:2101.02908
-
NVAE: A Deep Hierarchical Variational Autoencoder 8 Jul 2020 · 10 repositories · arXiv:2007.03898Syntology ran 23 of 41 samples · 18 unverified · 23 pointer-only (licence)
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Generation | 2 |
| Adversarial Attack | 1 |
| Anomaly Detection | 1 |
| Decoder | 1 |
| Diversity | 1 |
| Time Series | 1 |
| Time Series Analysis | 1 |
| Time Series Anomaly Detection | 1 |
| Unconditional Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections