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Learning with coarse labels
3260 papers • 126 benchmarks • 313 datasets
Learning fine-grained representation with coarsely-labelled dataset, which can significantly reduce the labelling cost. As a simple example, for the task of differentiation between different pets, we need a knowledgeable cat lover to distinguish between ‘British short’ and ‘Siamese’, but even a child annotator may help to discriminate between ‘cat’ and ‘non-cat’.
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Weakly Supervised Representation Learning with Coarse Labels
Juhua Hu, Hao Li, Yuanhong Xu, Qi Qian, Rong Jin•Mon May 18 2020
This work proposes an algorithm to learn the fine-grained patterns for the target task, when only its coarse-class labels are available, and provides a theoretical guarantee for this.
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