Collaborative learning with corrupted labels

Neural Networks, pp. 205-213, 2020.

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Abstract:

Deep neural networks (DNNs) have been very successful for supervised learning. However, their high generalization performance often comes with the high cost of annotating data manually. Collecting low-quality labeled dataset is relatively cheap, e.g., using web search engines, while DNNs tend to overfit to corrupted labels easily. In this...More

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