Papers with Code 아카이브
Semi-Supervised Image Classification
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- 평가표
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- 보존된 평가 기록
- 456
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/VisionLearningGroup/OP_Match
https://github.com/WGLab/SGAN
https://github.com/XG293/SupConLoss
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/Ximilar-com/tf-image
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YU1ut/MixMatch-pytorch
https://github.com/YaCpotato/B4ResearchDeepaugment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YaCpotato/deepaugmentFix
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/YanYan0716/MPL
https://github.com/Yangget/Mixup_All-use
https://github.com/YufeiHU-fr/obow_ssl
OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
https://github.com/YvanG/VQGAN-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZHKKKe/PixelSSL
https://github.com/ZackPashkin/text2cartoon-pytorch-CLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ZhimingZhou/AM-GAN
https://github.com/Zhongying-Deng/NorMatch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/a736875071/clip-vit-large-patch14
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/abbaasalif/freematch-improved
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
https://github.com/abcp4/DAPytorch
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/abhuse/cyclic-cosine-decay
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
https://github.com/acholston/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/acmiyaguchi/birdclef-2022
https://github.com/ae-foster/pytorch-simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/aelnouby/Text-to-Image-Synthesis
https://github.com/agupta231/CARROL
https://github.com/ai-forever/ru-clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/ajayjain/vectorascent
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/akjgskwi/mixup_chainer
https://github.com/akwasigroch/Pretext-Invariant-Representations
Self-Supervised Learning of Pretext-Invariant Representations
https://github.com/albumentations-team/albumentations
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/alexk1704/scclv2
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/alibaba/EasyCV
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/alibaba/EasyNLP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/amazon-science/semi-vit
https://github.com/ambujojha/SemiSupervisedLearning
https://github.com/amerch/CIFAR100-Training
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.