PV
AntixK/PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
7.6k 1.2k +6/wk
GitHub
architecture beta-vae celeba-dataset deep-learning dfc-vae gumbel-softmax iwae paper-implementations pytorch pytorch-implementation pytorch-vae reproducible-research
Trend
3
Star & Fork Trend (20 data points)
Stars
Forks
Multi-Source Signals
Growth Velocity
AntixK/PyTorch-VAE has +6 stars this period . 7-day velocity: 0.1%.
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| Metric | PyTorch-VAE | From-0-to-Research-Scientist-resources-guide | DeepLearning | deep-learning-coursera |
|---|---|---|---|---|
| Stars | 7.6k | 7.6k | 7.6k | 7.7k |
| Forks | 1.2k | 1.1k | 1.4k | 5.5k |
| Weekly Growth | +6 | -2 | +0 | +0 |
| Language | Python | N/A | Python | Jupyter Notebook |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | N/A | MIT | MIT |
Capability Radar vs From-0-to-Research-Scientist-resources-guide
PyTorch-VAE
From-0-to-Research-Scientist-resources-guide
Maintenance Activity 0
Last code push 383 days ago.
Community Engagement 78
Fork-to-star ratio: 15.6%. Active community forking and contributing.
Issue Burden 70
Issue data not yet available.
Growth Momentum 45
+6 stars this period — 0.08% growth rate.
License Clarity 95
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.