https://github.com/PaddlePaddle/PASSL/blob/main/docs/Train_CLIP_model.md
Learning Transferable Visual Models From Natural Language Supervision
Papers with Code 아카이브
Semi-Supervised Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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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/PaddlePaddle/PaddleClas
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/Pangoraw/GeoSSL
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/PaperCodeReview/BarlowTwins-TF
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/PaperCodeSubmission/OSPOT-VAE
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
https://github.com/PaulEmmanuelSotir/DeepCV
https://github.com/PhamNguyen97/TSA_pytorch
https://github.com/PhamQuocHuy1101/UDA-Image-classification
https://github.com/Pongpisit-Thanasutives/Multi-task-Physics-informed-neural-networks
https://github.com/PsorTheDoctor/microarray-data
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/RElbers/info-nce-pytorch
https://github.com/ReshinthAdith/BYOL-Pytorch
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/RohitDhankar/kaggle_wheat
https://github.com/Ryoo72/dimension-wise_mixup
https://github.com/S-HuaBomb/FixMatch-Paddle
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/SPEECHCOG/cpc_pytorch
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/SanghunYun/UDA_pytorch
https://github.com/SaraAmd/Semi-Supervised-Learning
https://github.com/SarthakYadav/audax
https://github.com/SeonghoBaek/CPC
Data-Efficient Image Recognition with Contrastive Predictive Coding
https://github.com/SeonghoBaek/FrameSequencePrediction
Data-Efficient Image Recognition with Contrastive Predictive Coding
https://github.com/SforAiDl/CountCLIP
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning
https://github.com/TOA-ZR/VATcode
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
https://github.com/TRAILab/ST-SLidR
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
https://github.com/TahmasbiM/Example
https://github.com/Taoudi/DataAugmentation
https://github.com/Taoudi/ImbalancedData
https://github.com/ThmCuong/IIC-alter
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
https://github.com/TianheWu/LGPNet
https://github.com/TomokiKomiya/SRGAN-keras
https://github.com/Trayan7/FixMatch-with-Soft-Labels
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/Valentyn1997/semi-supervised-text-classification
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.