improved_wgan_training
Code for reproducing experiments in "Improved Training of Wasserstein GANs" (by igul222)
HashGAN
HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN (by thuml)
| improved_wgan_training | HashGAN | |
|---|---|---|
| 2 | 1 | |
| 2,336 | 68 | |
| - | - | |
| 0.0 | 10.0 | |
| almost 6 years ago | over 5 years ago | |
| Python | Python | |
| MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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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.
improved_wgan_training
Posts with mentions or reviews of improved_wgan_training.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-08.
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[D] Is WGAN-GP gradient penalty applicable to the generator?
Code for https://arxiv.org/abs/1704.00028 found: https://github.com/igul222/improved_wgan_training
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[D] Is this what WGAN-GP's progression is supposed to look like?
In addition to the generator output layer differences already pointed out, there are likely other architectural differences. WGAN-GP has a fairly easy-to-use official codebase. I recommend running your dataset through it and verifying whether your implementation gets similar FIDs to it.
HashGAN
Posts with mentions or reviews of HashGAN.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-05-08.