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Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
[article]
2020
arXiv
pre-print
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the Shannon Entropy, but the side effect caused by entropy minimization, i.e., reduction of the prediction diversity, is mostly ignored. To address this issue, we reinvestigate the structure of classification output matrix of a randomly selected data
arXiv:2003.12237v1
fatcat:vojajtsdsvdwvk2umd3se4abgq