reasoning-9
Missing Labels
3260 papers • 126 benchmarks • 313 datasets
The challenge in multi-label learning with missing labels is that the training data often has incomplete label information. Collecting labels for multi-label datasets is a manual exercise and dependent on external sources, leading to the collection of only a subset of labels. This assumption of complete label information doesn't hold, especially when the label space is large. Inaccurate label-label and label-feature relationships can be captured, leading to suboptimal solutions in missing label settings.
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Multi-Label Learning from Single Positive Labels
P. Perona, Oisin Mac Aodha, Dan Morris, Elijah Cole, Titouan Lorieul, N. Jojic•Mon May 31 2021
This work considers the hardest version of this problem, where annotators provide only one relevant label for each image, and shows that in some cases it is possible to approach the performance of fully labeled classifiers despite training with significantly fewer confirmed labels.
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