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/juneweng/byol-pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/jwcalder/GraphLearningLayer
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/kakaobrain/scrl
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/kalelpark/FG-SSL
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/kawshik8/DL-project
Self-Supervised Learning of Pretext-Invariant Representations
https://github.com/kekmodel/FixMatch-pytorch
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/kekmodel/MPL-pytorch
https://github.com/kekmodel/UDA-pytorch
https://github.com/keras-team/keras-io/blob/master/examples/vision/nnclr.py
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
https://github.com/kevinghst/ICT
Interpolation Consistency Training for Semi-Supervised Learning
https://github.com/kevinghst/mixmatch
https://github.com/khanrc/pt.fractalnet
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/klemens-floege/oneprot
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/kornia/kornia
https://github.com/krafton-ai/mini-batch-cl
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/kschwethelm/hyperboliccv
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/ktran1/Manifold-attack
https://github.com/kynkaat/role-of-imagenet-classes-in-fid
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/lRomul/argus-freesound
https://github.com/lantgabor/Unsupervised-Data-Augmentation-PyTorch
https://github.com/leblancdaniel/paraphraser
https://github.com/lee-seon-woo/tscp2_pytorch
https://github.com/leehomyc/mixup_pytorch
https://github.com/leftthomas/SimCLR
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/leolee99/CLIP_ITM
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/leot13/BarlowTwins
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/lewis-morris/image_augment
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/lifeitech/fce-2d
https://github.com/lightly-ai/lightly
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/likelion-hyeonjun/VAT_PYTORCH
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/linear95/tc-estimation
https://github.com/linjieli222/hero_video_feature_extractor
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/liqunchen0606/triangle-gan
https://github.com/liuch37/image-processing
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/liuwei16/ALFNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.