https://github.com/deepmind/deepmind-research/tree/master/byol
공식 코드Bootstrap your own latent: A new approach to self-supervised Learning
Papers with Code 아카이브
Self-Supervised Person Re-Identification
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Currently, self-supervised representation learning is mainly tested on image classification tasks, which is not insufficient to verify its effectiveness. It should also be tested in the visual matching task, and pedestrian re-recognition is just such an appropriate task.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/moco
공식 코드https://github.com/google-research/simclr
공식 코드A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/wanggrun/triplet
공식 코드Solving Inefficiency of Self-supervised Representation Learning
https://github.com/1170500804/MoCo
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/2023-MindSpore-4/Code11/tree/main/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/63days/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AidenDurrant/MoCo-Pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AndrewAtanov/simclr-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/AndrewTal/MoCo-Pytorch
https://github.com/BalajiAI/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/ClaudiaShu/SSL-FER
https://github.com/CoinCheung/denseCL
https://github.com/Daniel-H-99/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/EchoItLiu/SelfGait
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/Evgeneus/MoCo
https://github.com/Ghiara/diva
https://github.com/HibikiJie/BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/HobbitLong/PyContrast
https://github.com/HobbitLong/SupContrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/JoonHyung-Park/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Kennethborup/BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/Liut2016/ecg-supcontrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Mayurji/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Mind23-2/MindCode-76
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Namkyeong/BGRL_Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/OML-Team/open-metric-learning
https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_BYOL_model.md
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/Pangoraw/GeoSSL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/PaperCodeReview/MoCo-TF
https://github.com/ReshinthAdith/BYOL-Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/SaeedShurrab/SimSiam-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/SaeedShurrab/Simple-BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/Seonghoon-Yu/MoCov2_Pytorch_tutorial
https://github.com/TRAILab/ST-SLidR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.