Semi-Supervised Image Classification (Cold Start)
1 papers with code • 8 benchmarks • 1 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.
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Image Classification (Cold Start)
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CIFAR-10
Most implemented papers
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
TonyLianLong/UnsupervisedSelectiveLabeling
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Intuitively, no matter what the downstream task is, instances to be labeled must be representative and diverse: The former would facilitate label propagation to unlabeled data, whereas the latter would ensure coverage of the entire dataset.
