https://github.com/AnnaManasyan/VICReg
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Papers with Code 아카이브
Semi-Supervised Image Classification
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Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.
You may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:
- An overview of proxy-label approaches for semi-supervised learning - Sebastian Ruder
- Semi-Supervised Learning in Computer Vision - Amit Chaudhary
<span style="color:grey; opacity: 0.6">( Image credit: Self-Supervised Semi-Supervised Learning )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BalajiAI/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Bennie-Han/Image-augementation-pytorch
https://github.com/BeyondCloud/Comp04_ReverseImageCaption
https://github.com/Brunogomes97/Imdb
https://github.com/CPUFronz/CapsVoxGAN
https://github.com/CaoShuning/MIXUP
https://github.com/Celiali/FixMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/ChingHuanChiu/sensitive
https://github.com/CoinCheung/fixmatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/CoinCheung/fixmatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/CuriousAI/ladder
https://github.com/DamianValle/ScarceClassifier
https://github.com/Daniel-H-99/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/DevD1092/face_emotion_recog
https://github.com/DonghwanKIM0101/CS492I_CV
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
https://github.com/EchoItLiu/SelfGait
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/FelixAbrahamsson/mixmatch-pytorch
https://github.com/FengHZ/AAAI2021-260
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
https://github.com/FloCF/SSL_pytorch
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/FreddeFrallan/Multilingual-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/GT-RIPL/FeatMatch
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
https://github.com/Gahyeonkim09/AAPL
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/GregorKobsik/Octree-Transformer
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/Hazelsuko07/InstaHide
https://github.com/HibikiJie/BYOL
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/HobbitLong/PyContrast
Self-Supervised Learning of Pretext-Invariant Representations
https://github.com/HobbitLong/SupContrast
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/IMvision12/keras-vision-models
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/INK-USC/DualRE
https://github.com/IgorSusmelj/barlowtwins
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL
https://github.com/Jeffkang-94/pytorch-MixMatch
https://github.com/JiwonCocoder/label_transformer
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
https://github.com/JohnYKiyo/VAT
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.