Semi-Supervised Image Classification
124 papers with code • 58 benchmarks • 13 datasets
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
( Image credit: Self-Supervised Semi-Supervised Learning )
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Image Classification
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Libraries
Use these libraries to find Semi-Supervised Image Classification models and implementations
CIFAR-10
ImageNet
CIFAR-100
SVHN
STL-10
Caltech-101
Caltech-256
Salinas
Semi-iNat
TMED
Latest papers
Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework
majidseydgar/Res-CP
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journal 2022
The generated pseudo labels of our proposed framework can be fed to various DNNs to improve their generalization capacity.
NP-Match: When Neural Processes meet Semi-Supervised Learning
Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Semi-supervised Learning (SSL) has witnessed great success owing to the impressive performances brought by various methods based on pseudo labeling and consistency regularization.
DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Following the success of supervised learning, semi-supervised learning (SSL) is now becoming increasingly popular.
Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations.
MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization
The core issue in semi-supervised learning (SSL) lies in how to effectively leverage unlabeled data, whereas most existing methods tend to put a great emphasis on the utilization of high-confidence samples yet seldom fully explore the usage of low-confidence samples.
SimMatch: Semi-supervised Learning with Similarity Matching
kylezheng1997/simmatch
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CVPR 2022
Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community.
Class-Aware Contrastive Semi-Supervised Learning
tencentyouturesearch/classification-semicls
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CVPR 2022
Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization.
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision
Discriminative self-supervised learning allows training models on any random group of internet images, and possibly recover salient information that helps differentiate between the images.
Debiased Self-Training for Semi-Supervised Learning
thuml/debiased-self-training
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Yet these datasets are time-consuming and labor-exhaustive to obtain on realistic tasks.
