https://github.com/FaceOnLive/Face-Recognition-SDK-Android
공식 코드Cross-Age Contrastive Learning for Age-Invariant Face Recognition
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/Fantasy-Studio/Paint-by-Example
공식 코드Paint by Example: Exemplar-based Image Editing with Diffusion Models
https://github.com/FeiiYin/StyleHEAT
공식 코드StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
https://github.com/FelipeNuti/diffusion-relative-rewards
공식 코드https://github.com/Fictionarry/InsTaG
공식 코드InsTaG: Learning Personalized 3D Talking Head from Few-Second Video
https://github.com/FineDiffusion/FineDiffusion
공식 코드FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes
https://github.com/FlyHighest/UnrealPerson
공식 코드UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification
https://github.com/ForawardStar/EDIT
공식 코드https://github.com/FoundationVision/Infinity
공식 코드Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
https://github.com/FoundationVision/VAR
공식 코드Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
https://github.com/Francisdadada/GRN
공식 코드https://github.com/Fu0511/XGrad-CAM
공식 코드Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
https://github.com/FuxiVirtualHuman/Write-a-Speaker
공식 코드Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation
https://github.com/G-U-N/Phased-Consistency-Model
공식 코드https://github.com/G-U-N/Stable-Consistency-Tuning
공식 코드Stable Consistency Tuning: Understanding and Improving Consistency Models
https://github.com/GAInuist/ConsDreamer
공식 코드ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation
https://github.com/GGorMM1/QIREN
공식 코드https://github.com/GH920/improved-bagan-gp
공식 코드https://github.com/GUIDesignResearch/GUIGAN
공식 코드GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks
https://github.com/GaParmar/clean-fid
공식 코드On Aliased Resizing and Surprising Subtleties in GAN Evaluation
https://github.com/GabrieleLozupone/LDAE
공식 코드https://github.com/GaoLii/DSP
공식 코드DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
https://github.com/Gengzigang/TokenSet
공식 코드https://github.com/GeorgeEskandar/USIS-Unsupervised-Semantic-Image-Synthesis
공식 코드https://github.com/Ghiara/diva
공식 코드https://github.com/GouravWadhwa/Hypergraphs-Image-Inpainting
공식 코드https://github.com/Greenere/use_classifier_as_generator
공식 코드https://github.com/GregorKobsik/Octree-Transformer
공식 코드Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences
https://github.com/Gsunshine/Enjoy-Hamburger
공식 코드https://github.com/GuHuangAI/ADM-Public
공식 코드Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
https://github.com/Guang000/Awesome-Dataset-Distillation
공식 코드https://github.com/Guim3/IcGAN
공식 코드https://github.com/GuoLanqing/ShadowDiffusion
공식 코드ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal
https://github.com/HE-diffusion/HE-diffusion
공식 코드Privacy-Preserving Diffusion Model Using Homomorphic Encryption
https://github.com/HLR/FoREST
공식 코드FoREST: Frame of Reference Evaluation in Spatial Reasoning Tasks
https://github.com/HReynaud/EchoDiffusion
공식 코드Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.