pytask
disentangling-vae
| pytask | disentangling-vae | |
|---|---|---|
| 1 | 1 | |
| 101 | 766 | |
| 5.0% | - | |
| 9.2 | 0.0 | |
| 7 days ago | over 1 year ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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pytask
disentangling-vae
-
[P] Python library for Variational Autoencoder benchmarking
There is a good repo of different beta-vae models here: https://github.com/YannDubs/disentangling-vae
What are some alternatives?
EvalAI - :cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI
Efficient-VDVAE - Official Pytorch and JAX implementation of "Efficient-VDVAE: Less is more"
torch-fidelity - High-fidelity performance metrics for generative models in PyTorch
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benchmark_VAE - Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
scvi-tools - Deep probabilistic analysis of single-cell and spatial omics data
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CelebAMask-HQ - A large-scale face dataset for face parsing, recognition, generation and editing.
minimal_VAE_on_Mario - A minimal VAE trained on Super Mario Bros levels.
Awesome-VAEs - A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
precision-recall-distributions - Assessing Generative Models via Precision and Recall (official repository)