https://github.com/Ash-Lee233/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AshikaAnand12/FaceManipulation
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/AshishSingh2261/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Ashwin-Pokharel/base_diffusion
https://github.com/AsianZeus/Ooze-Handwritten-Text-Generator
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Aspire-Mayank/Top-Deep-Learning-Stuffs
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/Atharva-Phatak/GAN_MNIST
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Atharva-Phatak/Image2Image
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atharva-Phatak/Season-Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Atmosphere-art/Self-Attention-GAN
https://github.com/AustinXY/mixnmatch_demo
MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://github.com/Avditvs/proGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/AverageName/Cycle_gan_pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AverageName/UI2IT
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Awadelrahman/GANPolypSeg
Generative Adversarial Networks for Automatic Polyp Segmentation
https://github.com/Ayush-Goyal-coding/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/B1ueber2y/NDiv
https://github.com/BAAI-DCAI/Training-Data-Synthesis
https://github.com/BH94/cGANs-tensorflow-Python
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BR-IDL/PaddleViT/blob/main/gan/Styleformer
Styleformer: Transformer based Generative Adversarial Networks with Style Vector
https://github.com/BR-IDL/PaddleViT/blob/main/gan/transGAN
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/Backdrop9019/adda_pytorch-pseudo-mixup-
https://github.com/Backdrop9019/pytorch_adda_mixup
https://github.com/Baichenjia/DCGAN-eager
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Baichenjia/Pix2Pix-eager
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Baichenjia/Resnet
https://github.com/Bakikii/ganillare
GANILLA: Generative Adversarial Networks for Image to Illustration Translation
https://github.com/Bakikii/stylegan2
https://github.com/Bakikii/stylegan2-pytorch23
https://github.com/BalajiAI/PyTorch-GAN/tree/main/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/BalrajAshwath/Adversarial-Domain_Adaptation_Paper_Implementations
https://github.com/Bao-Jiarong/ResNet
https://github.com/BaoGevvvv5/photo-restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.