https://github.com/cherise215/maxstyle
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-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/chingyaoc/DCL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/chorowski-lab/CPC_audio
https://github.com/chorowski-lab/hcpc
https://github.com/cjf8899/KeepAugment_Pytorch
KeepAugment: A Simple Information-Preserving Data Augmentation Approach
https://github.com/cjrd/self-supervised-pretraining
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/clabrugere/pytorch-scarf
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/clip-italian/clip-italian
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/code-implementation1/Code8/tree/main/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/cyrilvallez/image-manipulation-detection
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/dLutscher/MixMatch-TransferLearning
https://github.com/daixiangzi/ImprovedGan-pytorch
https://github.com/danielgordon10/vince
Self-Supervised Learning of Pretext-Invariant Representations
https://github.com/danielzgsilva/MOT
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/davidczy/supcon_gamma
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/davidtellez/contrastive-predictive-coding
https://github.com/deepak112/Keras-SRGAN
https://github.com/deepaks2112/vat_lds
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/delyan-boychev/grad-cache-con-learning
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/dewenzeng/clsp
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/dhansmair/flamingo-mini
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/dhx000/DGM_project
https://github.com/dibyadas/IRMAE
https://github.com/dishen12/Cuout_modify
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/divamgupta/ladder_network_keras
https://github.com/djsinghnegi/SRINImageEnhancemrntGAN
https://github.com/dongdong69/MixAugmentation
https://github.com/eaguaida/TF_EnsNet-
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/edreismd/convirt-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/edureisMD/ConVIRT-pytorch
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/eify/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/emadeldeen24/eval_ssl_ssc
https://github.com/eminorhan/baby-vision
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/eps696/aphantasia
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/erichson/noisy_mixup
https://github.com/ericyinyzy/vlattack
Learning Transferable Visual Models From Natural Language Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.