Semi-Supervised Image Classification
123 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
Meta Co-Training: Two Views are Better than One
jayrothenberger/meta-co-training
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We show that in the common case when independent views are not available we can construct such views inexpensively using pre-trained models.
SequenceMatch: Revisiting the design of weak-strong augmentations for Semi-supervised learning
By taking advantage of different augmentations and the consistency constraints between each pair of augmented examples, SequenceMatch helps reduce the divergence between the prediction distribution of the model for weakly and strongly augmented examples.
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
beandkay/epass
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In this paper, we propose a simple method named Ensemble Projectors Aided for Semi-supervised Learning (EPASS), which focuses mainly on improving the learned embeddings to boost the performance of the existing contrastive joint-training semi-supervised learning frameworks.
SemiReward: A General Reward Model for Semi-supervised Learning
The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias.
Towards Semi-supervised Learning with Non-random Missing Labels
njuyued/prg4ssl-mnar
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ICCV 2023
Semi-supervised learning (SSL) tackles the label missing problem by enabling the effective usage of unlabeled data.
SimMatchV2: Semi-Supervised Learning with Graph Consistency
kylezheng1997/simmatch
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ICCV 2023
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor.
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
LiheYoung/ShrinkMatch
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ICCV 2023
To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples.
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasible in time or cost.
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance.
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
yifanzhang-pro/relationmatch
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Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data.
