bojone/vae resource
a simple vae and cvae from keras observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3090
- days_rel: n/a
- days_push: 1933
- n_releases_24m: 0
Adoption not part of the score
1389 stars · 372 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A collection of simple Keras implementations of variational autoencoders (VAE) and conditional VAEs (CVAE), with example results on datasets like CelebA. It serves as reference/demo code accompanying blog posts on the author's site.
Use cases
- learn how to implement a vae in keras
- example code for conditional vae
- generate faces with a vae on celeba
- understand vae clustering with mnist
- starting point for building generative autoencoder models
When to choose
- you want minimal, readable VAE/CVAE reference implementations in Keras
- you are following the author's blog tutorials on VAEs
- you need a quick baseline for autoencoder experiments on older TensorFlow/Keras stacks
When to avoid
- you need a maintained, production-ready generative modeling library
- you use modern TensorFlow 2.x or PyTorch and need up-to-date APIs
- you need a license permitting redistribution
Facets
learning-resource · maturity maintenance
machine-learning deep-learning data-visualization deep-learning machine-learning image-processing python variational-autoencoder keras cvae example-code generative-models
1 source
- readme: https://github.com/bojone/vae · fetched 2026-08-28 · e4d36d6f8eb2
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| bojone/vae | main | 32 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem