https://github.com/josephtitan/diffloss
공식 코드DiffLoss: unleashing diffusion model as constraint for training image restoration network
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/joshi-bharat/deep_underwater_localization
공식 코드DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization
https://github.com/jotabravo/spacecraft-tta
공식 코드Test-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on Predicted-View Synthesis
https://github.com/joycenerd/p4d
공식 코드Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
https://github.com/joyhuyy1412/personahoi
공식 코드PersonaHOI: Effortlessly Improving Personalized Face with Human-Object Interaction Generation
https://github.com/jozenn/artistauditor
공식 코드ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models
https://github.com/jpzhang1810/ldm-robustness
공식 코드https://github.com/jqliu09/mcld
공식 코드Multi-focal Conditioned Latent Diffusion for Person Image Synthesis
https://github.com/jrohsc/backdororing_bias
공식 코드https://github.com/jsuarez5341/gan-you-do-the-gan-gan
공식 코드https://github.com/juanerx/q-dit
공식 코드Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
https://github.com/juice-jl/pressedjuice.jl
공식 코드https://github.com/julia0607/Wide-Range-Image-Blending
공식 코드https://github.com/juliawolleb/anomaly_berdiff
공식 코드Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection
https://github.com/julightzhong10/fastgan
공식 코드Improving the Speed and Quality of GAN by Adversarial Training
https://github.com/julschoen/temp-gan
공식 코드https://github.com/junjie-shentu/textual-localization
공식 코드Textual Localization: Decomposing Multi-concept Images for Subject-Driven Text-to-Image Generation
https://github.com/junjiehe96/AnyStory
공식 코드AnyStory: Towards Unified Single and Multiple Subject Personalization in Text-to-Image Generation
https://github.com/junjieyang97/meta-controlnet
공식 코드Meta ControlNet: Enhancing Task Adaptation via Meta Learning
https://github.com/junleen/Expression-manipulator
공식 코드https://github.com/junleen/RainNet
공식 코드Region-aware Adaptive Instance Normalization for Image Harmonization
https://github.com/junoh-kang/ogdm_edm
공식 코드https://github.com/junshutang/3DFaceShop
공식 코드3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
https://github.com/juntang-zhuang/TorchDiffEqPack
공식 코드MALI: A memory efficient and reverse accurate integrator for Neural ODEs
https://github.com/junting/seg2vid
공식 코드https://github.com/junwuzhang19/repaint123
공식 코드https://github.com/junyanz/BicycleGAN
공식 코드https://github.com/junyanz/CycleGAN
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/junyanz/VON
공식 코드Visual Object Networks: Image Generation with Disentangled 3D Representation
https://github.com/junyanz/iGAN
공식 코드Generative Visual Manipulation on the Natural Image Manifold
https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
공식 코드Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/junzhin/dgm-vlc
공식 코드Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning
https://github.com/justimyhxu/awesome-3d-generation
공식 코드https://github.com/justin941208/mapconnet
공식 코드MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning
https://github.com/justlfc03/tc-diffrecon
공식 코드https://github.com/jwyang/lr-gan.pytorch
공식 코드LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.