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/nvlabs/diffit
공식 코드https://github.com/nvlabs/edm
공식 코드Elucidating the Design Space of Diffusion-Based Generative Models
https://github.com/nvlabs/edm2
공식 코드Analyzing and Improving the Training Dynamics of Diffusion Models
https://github.com/nvlabs/imaginaire
공식 코드COCO-FUNIT: Few-Shot Unsupervised Image Translation with a Content Conditioned Style Encoder
https://github.com/nvlabs/t-stitch
공식 코드T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
https://github.com/nzilberstein/Repulsive-score-distillation-RSD-
공식 코드Repulsive Latent Score Distillation for Solving Inverse Problems
https://github.com/oanaignat/mosaig
공식 코드Multi-Agent Multimodal Models for Multicultural Text to Image Generation
https://github.com/ocatak/malware_api_class
공식 코드Data Augmentation Based Malware Detection using Convolutional Neural Networks
https://github.com/och-mac/tradiffusion
공식 코드TraDiffusion: Trajectory-Based Training-Free Image Generation
https://github.com/octree-nn/octfusion
공식 코드OctFusion: Octree-based Diffusion Models for 3D Shape Generation
https://github.com/odegeasslbc/FastGAN-pytorch
공식 코드Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
https://github.com/odegeasslbc/Self-Supervised-Sketch-to-Image-Synthesis-PyTorch
공식 코드https://github.com/ofa-sys/ofa
공식 코드https://github.com/ohad204/Lay-A-Scene
공식 코드Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
https://github.com/ohayonguy/PMRF
공식 코드Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
https://github.com/oho43/COW
공식 코드Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation
https://github.com/oldsweet/hsrmamba
공식 코드HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution
https://github.com/oliverrensu/flowar
공식 코드FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
https://github.com/oliverrensu/mvar
공식 코드M-VAR: Decoupled Scale-wise Autoregressive Modeling for High-Quality Image Generation
https://github.com/oliverrensu/mvg
공식 코드Medical Vision Generalist: Unifying Medical Imaging Tasks in Context
https://github.com/omerbt/MultiDiffusion
공식 코드MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
https://github.com/omerbt/Splice
공식 코드https://github.com/omeregev/click2mask
공식 코드https://github.com/omertov/encoder4editing
공식 코드https://github.com/omhall/collaborativeart
공식 코드Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative Models
https://github.com/omnai-lab/implicit-inversion
공식 코드https://github.com/omni-us/research-GANwriting
공식 코드GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images
https://github.com/omriav/blended-diffusion
공식 코드https://github.com/omriav/blended-latent-diffusion
공식 코드https://github.com/ondyari/FaceForensics
공식 코드FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/onefly123/PFDiff
공식 코드https://github.com/oneig-bench/oneig-benchmark
공식 코드OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation
https://github.com/onethousand1000/animportrait3d
공식 코드Text-based Animatable 3D Avatars with Morphable Model Alignment
https://github.com/onion-liu/BlendGAN
공식 코드BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/onuriel/PermutedAdaIN
공식 코드Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.