generative-adversarial-network

28 projetos partilham este topic do GitHub

generative-adversarial-network — the-gan-zoo ★14.7kgenerative-adversarial-networkstylegan2-pytorch — ★3.8kmakegirlsmoe_web — ★3.4kDeep-Learning-with-PyTorch-Tutorials — ★3.1kimg2img-turbo — ★2.5kAwesome-Text-to-Image — ★2.4ktext-to-image — ★2.2kmusegan — ★2kmmgeneration — ★2kgigagan-pytorch — ★1.9kganspace — ★1.8kgandissect — ★1.8klightweight-gan — ★1.7kdata-efficient-gans — ★1.3kPyTorch-RL — ★1.3kHyperGAN — ★1.2kexposure — ★781videogan — ★706HiDT — ★651Few-Shot-Patch-Based-Training — ★625LeakGAN — ★573T2F — ★546generative-compression — ★534TP-GAN — ★510BlendGAN — ★498vision-aided-gan — ★420DF-GAN — ★326SDGym — ★309stylegan2-pytorch★ 3.8kmakegirlsmoe_web★ 3.4kDeep-Learning-with-PyTor…★ 3.1kimg2img-turbo★ 2.5kAwesome-Text-to-Image★ 2.4ktext-to-image★ 2.2kmusegan★ 2kmmgeneration★ 2kgigagan-pytorch★ 1.9kganspace★ 1.8kgandissect★ 1.8klightweight-gan★ 1.7kdata-efficient-gans★ 1.3kPyTorch-RL★ 1.3kHyperGAN★ 1.2kexposure★ 781videogan★ 706HiDT★ 651Few-Shot-Patch-Based-Tra…★ 625LeakGAN★ 573T2F★ 546generative-compression★ 534TP-GAN★ 510BlendGAN★ 498vision-aided-gan★ 420DF-GAN★ 326SDGym★ 309

Linhas conectam membros que estão mensuravelmente relacionados entre si. O tamanho do ponto reflete estrelas.

🧬 Membros
the-gan-zoo
A list of all named GANs!
★ 14.7k
stylegan2-pytorch
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch.…
★ 3.8k
makegirlsmoe_web
Create Anime Characters with MakeGirlsMoe
★ 3.4k
Deep-Learning-with-PyTorch-Tutorials
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
★ 3.1k
img2img-turbo
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
★ 2.5k
Awesome-Text-to-Image
(ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
★ 2.4k
text-to-image
Text to image synthesis using thought vectors
★ 2.2k
musegan
An AI for Music Generation
★ 2k
mmgeneration
MMGeneration is a powerful toolkit for generative models, based on PyTorch and MMCV.
★ 2k
gigagan-pytorch
Implementation of GigaGAN, new SOTA GAN out of Adobe. Culmination of nearly a decade of research into GANs
★ 1.9k
ganspace
Discovering Interpretable GAN Controls [NeurIPS 2020]
★ 1.8k
gandissect
Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
★ 1.8k
lightweight-gan
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations…
★ 1.7k
data-efficient-gans
[NeurIPS 2020] Differentiable Augmentation for Data-Efficient GAN Training
★ 1.3k
PyTorch-RL
PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and…
★ 1.3k
HyperGAN
Composable GAN framework with api and user interface
★ 1.2k
exposure
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a…
★ 781
videogan
Generating Videos with Scene Dynamics. NIPS 2016.
★ 706
HiDT
Official repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
★ 651
Few-Shot-Patch-Based-Training
The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot…
★ 625
LeakGAN
The codes of paper "Long Text Generation via Adversarial Training with Leaked Information" on AAAI 2018. …
★ 573
T2F
T2F: text to face generation using Deep Learning
★ 546
generative-compression
TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression
★ 534
TP-GAN
Official TP-GAN Tensorflow implementation for paper "Beyond Face Rotation: Global and Local Perception GAN…
★ 510
BlendGAN
Official PyTorch implementation of "BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation"…
★ 498
vision-aided-gan
Ensembling Off-the-shelf Models for GAN Training (CVPR 2022 Oral)
★ 420
DF-GAN
[CVPR2022 oral] A Simple and Effective Baseline for Text-to-Image Synthesis
★ 326
SDGym
Benchmarking synthetic data generation methods.
★ 309
🔗 Familias relacionadas

Medido a partir dos tópicos do GitHub compartilhados por ambos os projetos, ponderado pela raridade de cada tópico.