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/IDEA-Research/HumanArt
공식 코드Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes
https://github.com/IDEA-Research/HumanSD
공식 코드HumanSD: A Native Skeleton-Guided Diffusion Model for Human Image Generation
https://github.com/IDKiro/MCT
공식 코드Multi-Curve Translator for High-Resolution Photorealistic Image Translation
https://github.com/IDKiro/sdxs
공식 코드SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions
https://github.com/IGLICT/DeepFaceDrawing-Jittor
공식 코드https://github.com/IPL-UV/rbig_jax
공식 코드https://github.com/IVRL/ComicsDepth
공식 코드https://github.com/IVRL/GANLocalEditing
공식 코드https://github.com/IamShubhamGupto/WSSL-Weighted-Self-Supervised-Learning-for-Image-Inpainting
공식 코드WSSL: Weighted Self-supervised Learning Framework For Image-inpainting
https://github.com/IdanAzuri/glico-learning-small-sample
공식 코드Generative Latent Implicit Conditional Optimization when Learning from Small Sample
https://github.com/IntMeGroup/MINT-IQA
공식 코드Quality Assessment for AI Generated Images with Instruction Tuning
https://github.com/InterDigitalInc/HRFAE
공식 코드https://github.com/IraKorshunova/bruno
공식 코드https://github.com/IshmaelBelghazi/ALI
공식 코드https://github.com/ItzikMalkiel/AGB
공식 코드Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation
https://github.com/IvanEz/Red-GAN
공식 코드https://github.com/IyatomiLab/LeafGAN
공식 코드LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis
https://github.com/JDAI-CV/FADA
공식 코드Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
https://github.com/JJ-ZHOU-Code/DAMM-Diffusion
공식 코드https://github.com/JOP-Lee/READ-Large-Scale-Neural-Scene-Rendering-for-Autonomous-Driving
공식 코드READ: Large-Scale Neural Scene Rendering for Autonomous Driving
https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/JStehouwer/FFD_CVPR2020
공식 코드https://github.com/JackLingjie/Textual-Aesthetics
공식 코드https://github.com/JarrentWu1031/CCPL
공식 코드CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer
https://github.com/Jeeseung-Park/Styleformer
공식 코드Styleformer: Transformer based Generative Adversarial Networks with Style Vector
https://github.com/JegZheng/LEGODiffusion
공식 코드https://github.com/JerrryNie/ConceptCLIP
공식 코드https://github.com/Jia1018/GoHD
공식 코드https://github.com/JiahuiYu/generative_inpainting
공식 코드https://github.com/Jian-danai/Decompose-Distill-BeautyGAN
공식 코드Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition
https://github.com/JianZhang-chick/EMOdiffhead
공식 코드EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion
https://github.com/JianghaoWu/FPL-UDA
공식 코드https://github.com/JiejiangWu/FaceG2E
공식 코드https://github.com/JindongGu/Awesome-Prompting-on-Vision-Language-Model
공식 코드A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
https://github.com/JindongJiang/GNM
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.