| lucidrains/stylegan2-pytorch |
3,147 |
|
0 |
2 |
over 3 years ago |
146 |
July 19, 2022 |
120 |
mit |
Python |
| Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement |
| AgaMiko/data-augmentation-review |
1,499 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
|
|
| List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others. |
| lucidrains/lightweight-gan |
1,368 |
|
0 |
0 |
over 3 years ago |
99 |
August 25, 2022 |
60 |
mit |
Python |
| Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two |
| mit-han-lab/data-efficient-gans |
1,231 |
|
0 |
0 |
over 2 years ago |
0 |
|
25 |
bsd-2-clause |
Python |
| [NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training |
| boschresearch/unetgan |
266 |
|
0 |
0 |
about 4 years ago |
0 |
|
7 |
other |
Python |
| Official Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020) |
| anton-jeran/FAST-RIR |
122 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
agpl-3.0 |
Python |
| This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. |
| bknyaz/sgg |
100 |
|
0 |
0 |
almost 3 years ago |
0 |
|
4 |
other |
Jupyter Notebook |
| Train Scene Graph Generation for Visual Genome and GQA in PyTorch >= 1.2 with improved zero and few-shot generalization. |
| luoxier/CycleGAN_Tensorlayer |
82 |
|
0 |
0 |
over 7 years ago |
0 |
|
6 |
mit |
Python |
| Re-implement CycleGAN in Tensorlayer |
| glam-imperial/EmotionalConversionStarGAN |
75 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
|
Python |
| This repository contains code to replicate results from the ICASSP 2020 paper "StarGAN for Emotional Speech Conversion: Validated by Data Augmentation of End-to-End Emotion Recognition". |
| VITA-Group/Ultra-Data-Efficient-GAN-Training |
62 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
mit |
Python |
| [NeurIPS'21] "Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly", Tianlong Chen, Yu Cheng, Zhe Gan, Jingjing Liu, Zhangyang Wang |
