https://github.com/lhaof/nudiff
공식 코드Diffusion-based Data Augmentation for Nuclei Image Segmentation
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/lhf12278/DRDL
공식 코드Dual-Stream Reciprocal Disentanglement Learning for Domain Adaptation Person Re-Identification
https://github.com/lhfazry/DropWiper
공식 코드Unsupervised Raindrop Removal from a Single Image using Conditional Diffusion Models
https://github.com/lhoyer/DAFormer
공식 코드https://github.com/lhoyer/hrda
공식 코드HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
https://github.com/lhoyer/mic
공식 코드MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
https://github.com/li-xirong/mmc-amd
공식 코드Learning Two-Stream CNN for Multi-Modal Age-related Macular Degeneration Categorization
https://github.com/li-zn-h/simworld
공식 코드SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model
https://github.com/liaohaofu/adn
공식 코드Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
https://github.com/liaopeiyuan/artbench
공식 코드The ArtBench Dataset: Benchmarking Generative Models with Artworks
https://github.com/lidan1/PhotoSketchMAN
공식 코드High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks
https://github.com/lifan724/magic_eraser
공식 코드MagicEraser: Erasing Any Objects via Semantics-Aware Control
https://github.com/lifangda01/adaptivesupervisedpatchnce
공식 코드https://github.com/lightchaserx/neural-global-shutter
공식 코드https://github.com/lilijiangg/autodiffusion
공식 코드https://github.com/lilyli2004/satire
공식 코드A Multi-Modal Method for Satire Detection using Textual and Visual Cues
https://github.com/limingcv/Photorealistic-Style-Transfer
공식 코드https://github.com/limuloo/3DIS
공식 코드3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation
https://github.com/limuloo/migc
공식 코드MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis
https://github.com/limuloo/pydiff
공식 코드https://github.com/lin-zhao-resolve/d3hr
공식 코드Taming Diffusion for Dataset Distillation with High Representativeness
https://github.com/lincoln20030413/ddsr
공식 코드https://github.com/linghuyuhangyuan/m2s
공식 코드Minutes to Seconds: Speeded-up DDPM-based Image Inpainting with Coarse-to-Fine Sampling
https://github.com/lingqiz/optimal-measurement
공식 코드Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic Models
https://github.com/lingxiao-li/bifrost
공식 코드BIFRÖST: 3D-Aware Image compositing with Language Instructions
https://github.com/lingxiao-li/hae
공식 코드The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation
https://github.com/linhaowei1/clog
공식 코드CLoG: Benchmarking Continual Learning of Image Generation Models
https://github.com/linjx-ustc1106/DosGAN-PyTorch
공식 코드https://github.com/linjx-ustc1106/TuiGAN-PyTorch
공식 코드TuiGAN: Learning Versatile Image-to-Image Translation with Two Unpaired Images
https://github.com/linjx-ustc1106/ZstGAN-PyTorch
공식 코드ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
https://github.com/lins-lab/gmem
공식 코드https://github.com/linsun449/iseg.code
공식 코드iSeg: An Iterative Refinement-based Framework for Training-free Segmentation
https://github.com/linwu-github/person-image-generation
공식 코드T-Person-GAN: Text-to-Person Image Generation with Identity-Consistency and Manifold Mix-Up
https://github.com/linzhiqiu/t2v_metrics
공식 코드GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
https://github.com/lipang/hsigene
공식 코드HSIGene: A Foundation Model For Hyperspectral Image Generation
https://github.com/lishaoxu1994/instant-nerf-stylization
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.