https://github.com/onwn/c2n
공식 코드C2N: Practical Generative Noise Modeling for Real-World Denoising
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/ood2iid/code
공식 코드Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
https://github.com/open-debin/mm2latent
공식 코드MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistance
https://github.com/open-mmlab/StyleShot
공식 코드https://github.com/open-mmlab/anycontrol
공식 코드AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation
https://github.com/open-mmlab/mmagic/tree/main/projects/powerpaint
공식 코드A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting
https://github.com/open-mmlab/mmediting
공식 코드https://github.com/open-mmlab/mmsegmentation
공식 코드https://github.com/openai/DALL-E
공식 코드https://github.com/openai/consistency_models
공식 코드https://github.com/openai/glide-text2im
공식 코드GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
https://github.com/openai/glow
공식 코드https://github.com/openai/guided-diffusion
공식 코드https://github.com/openai/improved-diffusion
공식 코드https://github.com/openai/improved-gan
공식 코드https://github.com/openai/improved_gan
공식 코드https://github.com/openai/pixel-cnn
공식 코드https://github.com/openai/sparse_attention
공식 코드https://github.com/openai/vdvae
공식 코드Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
https://github.com/openbmb/viscpm
공식 코드Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages
https://github.com/opengvlab/dcnv4
공식 코드Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications
https://github.com/opengvlab/instruct2act
공식 코드Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model
https://github.com/opengvlab/visionllm
공식 코드https://github.com/opensparsellms/skip-dit
공식 코드https://github.com/oppo-mente-lab/facescore
공식 코드FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
https://github.com/oppo-mente-lab/tlcm
공식 코드TLCM: Training-efficient Latent Consistency Model for Image Generation with 2-8 Steps
https://github.com/oppo-mente-lab/x2i
공식 코드https://github.com/oppo-us-research/FA-VAE
공식 코드Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder
https://github.com/optml-group/advunlearn
공식 코드Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
https://github.com/optml-group/bibaddiff
공식 코드From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion Models
https://github.com/optml-group/unlearn-saliency
공식 코드https://github.com/optml-group/unlearncanvas
공식 코드UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
https://github.com/orangesodahub/scenecraft
공식 코드https://github.com/orpatashnik/BalaGAN
공식 코드BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer
https://github.com/orpatashnik/StyleCLIP
공식 코드https://github.com/osu-nlp-group/magicbrush
공식 코드MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.