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/JingshuaiLiu/ASGAN
공식 코드Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks
https://github.com/JinshuChen/MOGAN
공식 코드MOGAN: Morphologic-structure-aware Generative Learning from a Single Image
https://github.com/Jireh-Jam/R-MNet-Inpainting-keras
공식 코드R-MNet: A Perceptual Adversarial Network for Image Inpainting
https://github.com/JiwanSeo/RAQ-VAE
공식 코드https://github.com/JonP07/Diffusion-PNO
공식 코드Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise Optimization
https://github.com/Jumponthemoon/WeatherEdit
공식 코드WeatherEdit: Controllable Weather Editing with 4D Gaussian Field
https://github.com/JungHeeKim29/DiffHDRsyn
공식 코드End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images
https://github.com/JunlinHan/CWR
공식 코드https://github.com/JunlinHan/DCLGAN
공식 코드Dual Contrastive Learning for Unsupervised Image-to-Image Translation
https://github.com/JunzheJosephZhu/HiFA
공식 코드HiFA: High-fidelity Text-to-3D Generation with Advanced Diffusion Guidance
https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets
공식 코드UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
https://github.com/Jyouhou/UnrealText
공식 코드UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
https://github.com/KAIST-VICLab/C-DiffSET
공식 코드https://github.com/KAIST-Visual-AI-Group/GrounDiT
공식 코드GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation
https://github.com/KU-CVLAB/3DFuse
공식 코드Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation
https://github.com/KU-CVLAB/DAG
공식 코드DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models
https://github.com/KU-CVLAB/InstaFormer
공식 코드InstaFormer: Instance-Aware Image-to-Image Translation with Transformer
https://github.com/KU-CVLAB/LANIT
공식 코드LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
https://github.com/KU-CVLAB/MoDiTalker
공식 코드MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation
https://github.com/KU-CVLAB/Perturbed-Attention-Guidance
공식 코드Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
https://github.com/KU-CVLAB/RetDream
공식 코드Retrieval-Augmented Score Distillation for Text-to-3D Generation
https://github.com/KU-CVLAB/Self-Attention-Guidance
공식 코드Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
https://github.com/KUCognitiveInformaticsLab/PairwiseExpressionAnnotator
공식 코드HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/Kaiseem/AD-GAN
공식 코드AD-GAN: End-to-end Unsupervised Nuclei Segmentation with Aligned Disentangling Training
https://github.com/Kaminyou/Dense-Normalization
공식 코드https://github.com/KarlXing/LUSR
공식 코드Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
https://github.com/Kayaba-Akihiko/next-bmdgan-tutorial
공식 코드https://github.com/KeleiHe/BiGL
공식 코드https://github.com/KelestZ/Latent2im
공식 코드Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
https://github.com/Kim-Dongjun/Soft-Truncation
공식 코드https://github.com/Kim-Dongjun/ctm-cifar10
공식 코드Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
https://github.com/Kim-Minseon/APGP
공식 코드Automatic Jailbreaking of the Text-to-Image Generative AI Systems
https://github.com/KishanKancharagunta/CSGAN
공식 코드CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation
https://github.com/KumapowerLIU/CSA-inpainting
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.