https://github.com/YichengDWu/tinyedm
Elucidating the Design Space of Diffusion-Based Generative Models
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/Yijunmaverick/GenerativeFaceCompletion
https://github.com/Yijunmaverick/UniversalStyleTransfer
https://github.com/YilinLiu97/Faster-DIP-Recon
https://github.com/YiteWang/CS547-final-project
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Yodai1996/CV
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Yotsuyubi/wave-nr-gan
https://github.com/YouYueHuang/CycleGAN_Unsupervised_Domain_Adaptation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Yougunhee/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/YoungWoong-Cho/Decay
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/YuanxunLu/LiveSpeechPortraits
Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/YuguangTong/improved_wgan_training
https://github.com/Yukino1010/WGAN-GP
https://github.com/YvanG/VQGAN-CLIP
https://github.com/Z-ypnos/SSDiff_main
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
https://github.com/ZC119/Handwritten-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ZHO-ZHO-ZHO/ComfyUI-Differential_Diffusion-Workflows
https://github.com/ZJULearning/diverse_image_synthesis
https://github.com/ZMpursue/Anime_avatar_generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ZTao-z/multiflow-resnet-ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZVK/Talking-Heads
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/ZVK/talking_heads
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/ZacharyGoshen/cifar-10-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zachdr1/CNNs
https://github.com/Zachdr1/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Zachdr1/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Zachdr1/WGAN
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
GANs N' Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)
https://github.com/Ze-Yang/Context-Transformer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Zhachory1/MusicNST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ZhangGongjie/CycleGAN-Pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Zhangyanbo/iResNetLab
https://github.com/ZhaoJ9014/face.evoLVe
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Zhaoyi-Yan/Shift-Net
https://github.com/ZhimingZhou/AM-GAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.