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/Wanger-SJTU/FCN-in-the-wild
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
https://github.com/WeiYangze/hibernate-demo
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Wence-May/Image-Annotation
https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR
https://github.com/Wenyuan-Vincent-Li/sinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Westlake-AI/openmixup
https://github.com/WillSuen/LostGANs
https://github.com/Windrill/Scenery-Video-GANs
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Windrill/small-scene-video-GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/WonwoongCho/Generative-Inpainting-pytorch
https://github.com/WuChenshen/MeRGAN
https://github.com/WuRui-Ella/Myfirststore
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/WuZhewei/my_First_GAN_tryout
https://github.com/X-LANCE/VoiceFlow-TTS
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/XHChen0528/SNGAN_Projection_Pytorch
https://github.com/XJhaoren/Academic-Group
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/XavierJiezou/cloud-removal-deploy
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/XavierXiao/Dreambooth-Stable-Diffusion
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/Xemnas0/NCSN-TF2.0
Generative Modeling by Estimating Gradients of the Data Distribution
https://github.com/XiafeiYu/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Xiang-cd/DiffEdit-stable-diffusion
DiffEdit: Diffusion-based semantic image editing with mask guidance
https://github.com/XiangGao1102/CTSS
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
https://github.com/XiangGao1102/FCDiffusion_code
Frequency-Controlled Diffusion Model for Versatile Text-Guided Image-to-Image Translation
https://github.com/XiangGao1102/PTDiffusion
https://github.com/Xiaomao136/stylegan2-faceswap
https://github.com/XieBaoshi/stylegan2
https://github.com/XiudingCai/PyTorch-EnCo
Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/Xnsam/clothing_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/YB221/Image-Inpainting-using-Partial-Convolutional-Layers
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/YHL04/ddpm
https://github.com/Yang-Q-D/mindsporeCode/tree/master/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Yash-10/modified_gravity_emulation
https://github.com/Yashgh7076/CPSC-8810-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/YeongHyeon/ADAE-TF
https://github.com/YeshengSu/EdgeConnect
Image Inpainting for Irregular Holes Using Partial Convolutions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.