awesome-generative-modeling
Bolei's archive on generative modeling (by zhoubolei)
vaegan
An implementation of VAEGAN (variational autoencoder + generative adversarial network). (by anitan0925)
| awesome-generative-modeling | vaegan | |
|---|---|---|
| 1 | 1 | |
| 159 | 91 | |
| - | - | |
| 10.0 | 10.0 | |
| about 3 years ago | about 7 years ago | |
| Python | ||
| - | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
awesome-generative-modeling
Posts with mentions or reviews of awesome-generative-modeling.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-18.
-
[D] My embarrassing trouble with inverting a GAN generator. Do GAN questions still get answered? ;-)
Found relevant code at https://github.com/zhoubolei/awesome-generative-modeling + all code implementations here
vaegan
Posts with mentions or reviews of vaegan.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-18.
-
[D] My embarrassing trouble with inverting a GAN generator. Do GAN questions still get answered? ;-)
What about a VAE-GAN? It couples a VAE (encoder-decoder) network with a GAN by sharing weights between the generator and decoder. This way you can you use the encoder to obtain the latent variable of interest.
What are some alternatives?
When comparing awesome-generative-modeling and vaegan you can also consider the following projects:
lightweight-gan - Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two
GlowIP - Code to reproduce results from "Invertible generative models for inverse problems: mitigating representation error and dataset bias"