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/muzairkhattak/multimodal-prompt-learning
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/nahidalam/open_clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/namakemono/kaggle-alaska2-image-steganalysis
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/nanwei1/MNIST_GAN
https://github.com/narendoraiswamy/MixMatch-pytorch-demo
https://github.com/nathanin/IIC
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
https://github.com/naver/tldr
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
https://github.com/nikheelpandey/TAUP
Big Self-Supervised Models are Strong Semi-Supervised Learners
https://github.com/nikheelpandey/TAUP-PyTorch
Big Self-Supervised Models are Strong Semi-Supervised Learners
https://github.com/nikolaev-n/CocktailPartySpeakerRecognition
https://github.com/ninatu/mood_challenge
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/noachr/MixMatch-fastai
https://github.com/nopperl/clip_arxiv_pmc
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/notha99y/mean_teacher_domain_adapt
https://github.com/ntozer/mixmatch-tensorflow2.0
https://github.com/nupurkmr9/vision-aided-gan
https://github.com/nzw0301/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/open-mmlab/mmdetection
Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
https://github.com/open-mmlab/mmselfsup
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/optimization-ai/sogclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/oxygen0605/ImageClassification
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/p3i0t/simclr-cifar10
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/parkinkon1/simclr
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/peisuke/UnsupervisedDataAugmentation.pytorch
https://github.com/perrying/realistic-ssl-evaluation-pytorch
Interpolation Consistency Training for Semi-Supervised Learning
https://github.com/phanav/fixmatch-fastai
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/phanav/simclr-presentation
A Simple Framework for Contrastive Learning of Visual Representations
https://github.com/philip-bachman/amdim-public
Data-Efficient Image Recognition with Contrastive Predictive Coding
https://github.com/philippmwirth/byol
Bootstrap your own latent: A new approach to self-supervised Learning
https://github.com/prabhupad26/100daysofML
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pseulki/rococo
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/psr007ps/NeuralNetAugmentations
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/ptrckhmmr/learning-to-defer-with-limited-expert-predictions
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
https://github.com/pwc-1/Paper-8/tree/main/clip
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/pytorch/vision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.