PyTorch VAE
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Overview
HEALTH SCORE
Critical*
Partial score (2/3 categories)
15out of 65
Development cadence is the primary crisis signal. Maintainer coverage shows little to offset it. Every health signal is at or near zero. Security & Supply Chain is unavailable, so weights were redistributed.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Critical*(15/65)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
No PRs or issues were opened in the review window, so response time could not be measured.
Bus factor
No data available. Repository has not been indexed for bus factor.
Organization Diversity
Contributors span 22 organizations.
Project Achievements
Leaderboards
About
A PyTorch implementation of various Variational Autoencoders (VAEs), providing a collection of VAE architectures and their variants for deep generative modeling and representation learning
First commit
Software value
$141K
Tags
architecture
beta-vae
celeba-dataset
deep-learning
dfc-vae
gumbel-softmax
iwae
paper-implementations
pytorch
pytorch-implementation
pytorch-vae
reproducible-research
vae
vae-implementation
variational-autoencoders
vqvae
wae
License(s)
Apache-2.0
Links
Data sources
Git
GitHub