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/ZAL-NASK/CSMC
공식 코드https://github.com/ZEISS/iPad_TrueDepth_Issue_App
공식 코드On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations
https://github.com/ZEISS/iPad_TrueDepth_Issue_Eval
공식 코드On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations
https://github.com/ZENGXH/NPDRAW
공식 코드NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation
https://github.com/ZGCTroy/ED-DPM
공식 코드Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation
https://github.com/ZHKKKe/NeuralPreset
공식 코드https://github.com/ZKI-PH-ImageAnalysis/GANetic-Loss
공식 코드GANetic Loss for Generative Adversarial Networks with a Focus on Medical Applications
https://github.com/ZPdesu/SEAN
공식 코드SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
https://github.com/ZZUTK/Decoupled-Learning-Conditional-GAN
공식 코드https://github.com/Zeqiang-Lai/MiniDALLE-3
공식 코드Mini-DALLE3: Interactive Text to Image by Prompting Large Language Models
https://github.com/Zhangjinso/PINet_PG
공식 코드https://github.com/Zhangjinso/PISE
공식 코드https://github.com/Zhangjinso/PoNA
공식 코드PoNA: Pose-guided Non-local Attention for Human Pose Transfer
https://github.com/Zhangwenyao1/DreamVLA
공식 코드DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
https://github.com/ZhaoJ9014/High_Performance_Face_Recognition
공식 코드https://github.com/Zhaoyi-Yan/Shift-Net_pytorch
공식 코드https://github.com/ZhenbangDu/Reliable_AD
공식 코드Towards Reliable Advertising Image Generation Using Human Feedback
https://github.com/Zhendong-Wang/Diffusion-GAN
공식 코드https://github.com/Zheng222/DMFN
공식 코드https://github.com/Zhenhang-Li/GlyphOnly
공식 코드First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text Blending
https://github.com/ZhiwenShao/ADLD
공식 코드Unconstrained Facial Action Unit Detection via Latent Feature Domain
https://github.com/ZichengDuan/EZIGen
공식 코드https://github.com/ZiqiaoPeng/SyncTalk
공식 코드SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
https://github.com/ZongyuGuo/Inpainting_FRRN
공식 코드Progressive Image Inpainting with Full-Resolution Residual Network
https://github.com/Zyriix/GDD
공식 코드https://github.com/a-mos/High_Resolution_Image_Inpainting
공식 코드https://github.com/a-raniy-day/sita
공식 코드SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image Generation
https://github.com/a43992899/marble-benchmark
공식 코드MARBLE: Music Audio Representation Benchmark for Universal Evaluation
https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction
공식 코드https://github.com/a514514772/hijackgan
공식 코드https://github.com/aaaaaasuka/impress
공식 코드https://github.com/aaberdam/AdaLISTA
공식 코드https://github.com/aaleka/retree
공식 코드Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation
https://github.com/aaltoml/generative-inverse-heat-dissipation
공식 코드https://github.com/aamir-mustafa/Transformation-CR
공식 코드Transformation Consistency Regularization- A Semi-Supervised Paradigm for Image-to-Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.