https://github.com/lissomx/MSP
공식 코드Latent Space Factorisation and Manipulation via Matrix Subspace Projection
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/little-misfit/survey-of-virtual-try-on
공식 코드https://github.com/littlebeen/Diffusion-Enhancement-for-CR
공식 코드Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
https://github.com/littlewhitesea/360pant
공식 코드360PanT: Training-Free Text-Driven 360-Degree Panorama-to-Panorama Translation
https://github.com/litwellchi/chatillusion
공식 코드M$^{2}$Chat: Empowering VLM for Multimodal LLM Interleaved Text-Image Generation
https://github.com/liudan193/Fairness-Benchmark-for-Face-Forgery-Detection
공식 코드Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations
https://github.com/liuff19/Sherpa3D
공식 코드Sherpa3D: Boosting High-Fidelity Text-to-3D Generation via Coarse 3D Prior
https://github.com/liujin112/zepo
공식 코드https://github.com/liuqk3/put
공식 코드Reduce Information Loss in Transformers for Pluralistic Image Inpainting
https://github.com/liusulin/ddpd
공식 코드Think While You Generate: Discrete Diffusion with Planned Denoising
https://github.com/liutaocode/DiffDub
공식 코드DiffDub: Person-generic Visual Dubbing Using Inpainting Renderer with Diffusion Auto-encoder
https://github.com/liuxiao-guan/iet_agc
공식 코드Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models
https://github.com/liuyixin-louis/diffshortcut
공식 코드Rethinking and Defending Protective Perturbation in Personalized Diffusion Models
https://github.com/liuyuan-pal/syncdreamer
공식 코드SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
https://github.com/liuyvchi/mie-x
공식 코드How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
https://github.com/liuziyu77/visual-rft
공식 코드https://github.com/liwd190019/shallow-diffuse
공식 코드https://github.com/liwrui/scenedreamer360
공식 코드SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splatting
https://github.com/liyuantsao/BFSR
공식 코드Boosting Flow-based Generative Super-Resolution Models via Learned Prior
https://github.com/liyunsheng13/BDL
공식 코드Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/lizs17/gip_cardiac_mri
공식 코드Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI Reconstruction
https://github.com/ljh0v0/fmd-frechet-motion-distance
공식 코드Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/ljjcoder/ehtdi
공식 코드https://github.com/ljl5261/mmm-rs
공식 코드https://github.com/ljm565/tesgan
공식 코드https://github.com/lllyasviel/controlnet
공식 코드Adding Conditional Control to Text-to-Image Diffusion Models
https://github.com/llm-conditioned-diffusion/llm-conditioned-diffusion.github.io
공식 코드https://github.com/llm-lab-org/generative-ai-for-character-animation-survey
공식 코드https://github.com/llmir/yolocurvseg
공식 코드YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation
https://github.com/locatedit/locatedit
공식 코드LOCATEdit: Graph Laplacian Optimized Cross Attention for Localized Text-Guided Image Editing
https://github.com/locuslab/diffusion-model-hallucination
공식 코드Understanding Hallucinations in Diffusion Models through Mode Interpolation
https://github.com/locuslab/ect
공식 코드https://github.com/locuslab/smoothinv
공식 코드Single Image Backdoor Inversion via Robust Smoothed Classifiers
https://github.com/logsanand/Monocular-depth-UAVimages
공식 코드Self-supervised monocular depth estimation from oblique UAV videos
https://github.com/lorafib/LumiPath
공식 코드LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
https://github.com/lorisnanni/feature-transforms-for-image-data-augmentation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.