semi-supervised-image-classification
Semi-Supervised Image Classification (Cold Start)
3260 papers • 126 benchmarks • 313 datasets
This is the same as the semi-supervised image classification task, with the key difference being that the labelled subset chosen needs to be selection in a class agnostic manner. This means that the standard practice in semi-supervised learning of using a random class stratified sample is "cheating" in this case, as class information is required for the whole dataset for this to be done. Rather, this challenge requires a smart cold-start or unsupervised selective labelling strategy to identify images that are most informative and result in the best performing models.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in semi-supervised-image-classification
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Dataset
Best Model
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CIFAR-10, 100 Labels
CIFAR-10, 100 Labels
CIFAR-10, 30 Labels
CIFAR-10, 30 Labels
Libraries
iUse these libraries to find semi-supervised-image-classification models and implementations
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No subtasks available.
Most implemented papers
Unsupervised Selective Labeling for More Effective Semi-supervised Learning
Xudong Wang, Long Lian, Stella X. Yu•Tue Oct 05 2021
This work sets a new standard for practical and efficient semi-supervised learning on partially labeled data, and consistently improves SSL methods over state-of-the-art active learning given labeled data by 8 to 25 times in label efficiency.
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