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Repairing Group-Level Errors for DNNs Using Weighted Regularization. (arXiv:2203.13612v1 [cs.LG])
March 28, 2022, 1:11 a.m. | Ziyuan Zhong, Yuchi Tian, Conor J.Sweeney, Vicente Ordonez-Roman, Baishakhi Ray
cs.LG updates on arXiv.org arxiv.org
Deep Neural Networks (DNNs) have been widely used in software making
decisions impacting people's lives. However, they have been found to exhibit
severe erroneous behaviors that may lead to unfortunate outcomes. Previous work
shows that such misbehaviors often occur due to class property violations
rather than errors on a single image. Although methods for detecting such
errors have been proposed, fixing them has not been studied so far. Here, we
propose a generic method called Weighted Regularization (WR) consisting of …
More from arxiv.org / cs.LG updates on arXiv.org
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