OneshottranslationPytorch implementation of "One-Shot Unsupervised Cross Domain Translation" NIPS 2018
Stars: ✭ 135 (-25%)
Lggan[CVPR 2020] Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
Stars: ✭ 97 (-46.11%)
ExprganFacial Expression Editing with Controllable Expression Intensity
Stars: ✭ 98 (-45.56%)
automatic-manga-colorizationUse keras.js and cyclegan-keras to colorize manga automatically. All computation in browser. Demo is online:
Stars: ✭ 20 (-88.89%)
Pix2pixImage-to-image translation with conditional adversarial nets
Stars: ✭ 8,765 (+4769.44%)
Anycost Gan[CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing
Stars: ✭ 367 (+103.89%)
UEGAN[TIP2020] Pytorch implementation of "Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network"
Stars: ✭ 68 (-62.22%)
HidtOfficial repository for the paper "High-Resolution Daytime Translation Without Domain Labels" (CVPR2020, Oral)
Stars: ✭ 513 (+185%)
CycleganSoftware that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
Stars: ✭ 10,933 (+5973.89%)
Tsit[ECCV 2020 Spotlight] A Simple and Versatile Framework for Image-to-Image Translation
Stars: ✭ 141 (-21.67%)
ADL2019Applied Deep Learning (2019 Spring) @ NTU
Stars: ✭ 20 (-88.89%)
lecam-ganRegularizing Generative Adversarial Networks under Limited Data (CVPR 2021)
Stars: ✭ 127 (-29.44%)
AODAOfficial implementation of "Adversarial Open Domain Adaptation for Sketch-to-Photo Synthesis"(WACV 2022/CVPRW 2021)
Stars: ✭ 44 (-75.56%)
AsymmetricGAN[ACCV 2018 Oral] Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
Stars: ✭ 42 (-76.67%)
Few Shot Patch Based TrainingThe official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training
Stars: ✭ 313 (+73.89%)
Selectiongan[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation
Stars: ✭ 366 (+103.33%)
Enlightengan[IEEE TIP'2021] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang
Stars: ✭ 434 (+141.11%)
SDEditPyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
Stars: ✭ 394 (+118.89%)
Inpainting gmcnnImage Inpainting via Generative Multi-column Convolutional Neural Networks, NeurIPS2018
Stars: ✭ 256 (+42.22%)
ApdrawingganCode for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)
Stars: ✭ 510 (+183.33%)
All About The GanAll About the GANs(Generative Adversarial Networks) - Summarized lists for GAN
Stars: ✭ 630 (+250%)
catgan pytorchUnsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Stars: ✭ 50 (-72.22%)
PerceptualGANPytorch implementation of Image Manipulation with Perceptual Discriminators paper
Stars: ✭ 119 (-33.89%)
HistoGANReference code for the paper HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms (CVPR 2021).
Stars: ✭ 158 (-12.22%)
VSGANVapourSynth Single Image Super-Resolution Generative Adversarial Network (GAN)
Stars: ✭ 124 (-31.11%)
Discogan PytorchPyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
Stars: ✭ 961 (+433.89%)
ConsinganPyTorch implementation of "Improved Techniques for Training Single-Image GANs" (WACV-21)
Stars: ✭ 294 (+63.33%)
Faceswap GanA denoising autoencoder + adversarial losses and attention mechanisms for face swapping.
Stars: ✭ 3,099 (+1621.67%)
Texturize🤖🖌️ Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture.
Stars: ✭ 366 (+103.33%)
IganInteractive Image Generation via Generative Adversarial Networks
Stars: ✭ 3,845 (+2036.11%)
SeanSEAN: Image Synthesis with Semantic Region-Adaptive Normalization (CVPR 2020, Oral)
Stars: ✭ 387 (+115%)
ConganContinious Generative Adversarial Network
Stars: ✭ 41 (-77.22%)
HyperganComposable GAN framework with api and user interface
Stars: ✭ 1,104 (+513.33%)
Image To Image Papers🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)
Stars: ✭ 949 (+427.22%)
Domain Transfer NetworkTensorFlow Implementation of Unsupervised Cross-Domain Image Generation
Stars: ✭ 850 (+372.22%)
Contrastive Unpaired TranslationContrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
Stars: ✭ 822 (+356.67%)
GanspaceDiscovering Interpretable GAN Controls [NeurIPS 2020]
Stars: ✭ 1,224 (+580%)
Context Encoder[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs
Stars: ✭ 731 (+306.11%)
Dcgan TensorflowA Tensorflow implementation of Deep Convolutional Generative Adversarial Networks trained on Fashion-MNIST, CIFAR-10, etc.
Stars: ✭ 70 (-61.11%)
Neural DoodleTurn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos, transfer style from one image to another, perform example-based upscaling, but wait... there's more! (An implementation of Semantic Style Transfer.)
Stars: ✭ 9,680 (+5277.78%)
Lr Gan.pytorchPytorch code for our ICLR 2017 paper "Layered-Recursive GAN for image generation"
Stars: ✭ 145 (-19.44%)
GandissectPytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/
Stars: ✭ 1,700 (+844.44%)
Gesturegan[ACM MM 2018 Oral] GestureGAN for Hand Gesture-to-Gesture Translation in the Wild
Stars: ✭ 136 (-24.44%)
UnetganOfficial Implementation of the paper "A U-Net Based Discriminator for Generative Adversarial Networks" (CVPR 2020)
Stars: ✭ 139 (-22.78%)
CoCosNet-v2CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
Stars: ✭ 312 (+73.33%)
texturize🤖🖌️ Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture.
Stars: ✭ 495 (+175%)
MixnmatchPytorch implementation of MixNMatch
Stars: ✭ 694 (+285.56%)
Mlds2018springMachine Learning and having it Deep and Structured (MLDS) in 2018 spring
Stars: ✭ 124 (-31.11%)
StarnetStarNet
Stars: ✭ 141 (-21.67%)