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/Yan-Xia/RealPoint3D
공식 코드RealPoint3D: Point Cloud Generation from a Single Image with Complex Background
https://github.com/Yan98/S2FGAN
공식 코드S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation
https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy
공식 코드Diffusion Models: A Comprehensive Survey of Methods and Applications
https://github.com/YangLiu9208/SAKDN
공식 코드Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
https://github.com/YangNaruto/FQ-GAN
공식 코드https://github.com/YangNaruto/latent-energy-transport
공식 코드Unpaired Image-to-Image Translation via Latent Energy Transport
https://github.com/YangZhang4065/AdaptationSeg
공식 코드Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
https://github.com/YanzuoLu/CFLD
공식 코드Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis
https://github.com/YashDeo-York/GenMed
공식 코드Metrics that matter: Evaluating image quality metrics for medical image generation
https://github.com/YeLuoSuiYou/openstorypp
공식 코드Openstory++: A Large-scale Dataset and Benchmark for Instance-aware Open-domain Visual Storytelling
https://github.com/YesianRohn/TextSSR
공식 코드TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
https://github.com/Yi-Shi94/AMDM
공식 코드Interactive Character Control with Auto-Regressive Motion Diffusion Models
https://github.com/Yikai-Wang/SeMani/tree/main/SeMani-Trans
공식 코드https://github.com/YilinLiu97/Untrained-Recon
공식 코드https://github.com/YingdaXia/SynthCP
공식 코드Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentation
https://github.com/YingzhenLi/SteinGrad
공식 코드https://github.com/Yonsei-STL/pix2next
공식 코드Pix2Next: Leveraging Vision Foundation Models for RGB to NIR Image Translation
https://github.com/YooJiHyeong/SinIR
공식 코드SinIR: Efficient General Image Manipulation with Single Image Reconstruction
https://github.com/YoungGod/sturcture-inpainting
공식 코드Learning to Incorporate Structure Knowledge for Image Inpainting
https://github.com/YoushanZhang/CoxImgSwinTransformer
공식 코드Complex Image Generation SwinTransformer Network for Audio Denoising
https://github.com/Yqcca/CMLoRA
공식 코드Cached Multi-Lora Composition for Multi-Concept Image Generation
https://github.com/Ysz2022/Fourier123
공식 코드Hybrid Fourier Score Distillation for Efficient One Image to 3D Object Generation
https://github.com/YuYin1/DA-GAN
공식 코드https://github.com/YuanXue1993/WireframeRenderer
공식 코드Neural Wireframe Renderer: Learning Wireframe to Image Translations
https://github.com/Yuanshi9815/OminiControl
공식 코드OminiControl2: Efficient Conditioning for Diffusion Transformers
https://github.com/Yuantian013/E2GAN
공식 코드Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
https://github.com/YudongGuo/AD-NeRF
공식 코드AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
https://github.com/YueWuHKUST/AniFaceGAN
공식 코드AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars
https://github.com/Yuheng-Li/MixNMatch
공식 코드MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
https://github.com/YulongBonjour/BrainCLIP
공식 코드https://github.com/YunChunChen/NAS-DIP-pytorch
공식 코드NAS-DIP: Learning Deep Image Prior with Neural Architecture Search
https://github.com/Yushi-Hu/tifa
공식 코드TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
https://github.com/YuvalNirkin/fsgan
공식 코드https://github.com/Yux1angJi/DIFF
공식 코드Diffusion Features to Bridge Domain Gap for Semantic Segmentation
https://github.com/YuxinWenRick/tree-ring-watermark
공식 코드Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.