2021
DOI: 10.48550/arxiv.2102.06735
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Learning Deep Neural Networks under Agnostic Corrupted Supervision

Boyang Liu,
Mengying Sun,
Ding Wang
et al.

Abstract: Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption, and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e.g., based on their individual los… Show more

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