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
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.
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution
jaeill/CVPR23-VNE
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CVPR 2023
Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation.
NP-Match: Towards a New Probabilistic Model for Semi-Supervised Learning
In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match.
Learning Customized Visual Models with Retrieval-Augmented Knowledge
Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability.
SVFormer: Semi-supervised Video Transformer for Action Recognition
chenhsing/svformer
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CVPR 2023
In this paper, we investigate the use of transformer models under the SSL setting for action recognition.
Semi-Supervised Single-View 3D Reconstruction via Prototype Shape Priors
chenhsing/ssp3d
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In particular, we introduce an attention-guided prototype shape prior module for guiding realistic object reconstruction.
OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification
Data mixing, or mixup, is a data-dependent augmentation technique that has greatly enhanced the generalizability of modern deep neural networks.
USB: A Unified Semi-supervised Learning Benchmark for Classification
microsoft/semi-supervised-learning
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We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
Semi-supervised Vision Transformers at Scale
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks.
RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learning
In this work, we propose Reciprocal Distribution Alignment (RDA) to address semi-supervised learning (SSL), which is a hyperparameter-free framework that is independent of confidence threshold and works with both the matched (conventionally) and the mismatched class distributions.
