data-efficient-gans
Fast-SRGAN
| data-efficient-gans | Fast-SRGAN | |
|---|---|---|
| 9 | - | |
| 1,258 | 638 | |
| 0.2% | - | |
| 0.0 | 0.0 | |
| 6 months ago | about 2 months ago | |
| Python | Python | |
| BSD 2-clause "Simplified" License | MIT License |
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.
data-efficient-gans
-
[D] Has anyone tried GAN "tricks" on VAEs?
Code for https://arxiv.org/abs/2006.10738 found: https://github.com/mit-han-lab/data-efficient-gans
-
What StyleGan model to use for a custom dataset of small size?
I would like to make a tiny project with GANs using some high quality pictures of a single individual. I am planning to get around 500 of these and then x-flip them, however I am not sure what model I should consider for the training. I have used StyleGan2 ADA for another project which ended quite well, but I had around 14k pictures, here now the training size is much smaller and I was therefore thinking about using DiffAugment which has seemingly promising results with just 100 images.
-
This Bot Crime Did Not Occur
I used a modified version of this repo, and there's also the official NVIDIA implementation, though neither have official notebooks. You can Google 'StyleGAN2 ADA Colab' and find a few starting points that way, but wait a few hours and I can clean up my notebook and post it here!
-
[P] Differentiable augmentation for GANs - Implementation and explanation
Paper: https://arxiv.org/abs/2006.10738
-
Deepspeed x Stylegan?
There are some repos which I've looked at to add deepspeed to such as DiffAugment-stylegan2-pytorch, lucidrains/stylegan2-pytorch and eps696/stylegan2 (which is in tensorflow so it would need to be translated to pytorch as deepspeed only works with pytorch right now).
-
Model takes seconds to train per epoch with 1 accuracy
Here is the paper using GANs with few data points https://arxiv.org/abs/2006.10738
-
Looking for resources regarding GANs trained on my own stuff.
Hey, for image gans, you can use smooth data aumentation https://github.com/mit-han-lab/data-efficient-gans in case you have a reasonable sized dataset.
Fast-SRGAN
We haven't tracked posts mentioning Fast-SRGAN yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation
a-PyTorch-Tutorial-to-Super-Resolution - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network | a PyTorch Tutorial to Super-Resolution
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
awesome-colab-notebooks - Collection of google colaboratory notebooks for fast and easy experiments
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
iSeeBetter - iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
gansformer - Generative Adversarial Transformers
super-resolution - Tensorflow 2.x based implementation of EDSR, WDSR and SRGAN for single image super-resolution
generative_inpainting - DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral
CleanTF2plus - Clean TF2's sequel
cartoonize - A demo webapp to convert images and videos into cartoon!
EDSR_Tensorflow - TensorFlow implementation of 'Enhanced Deep Residual Networks for Single Image Super-Resolution'.