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Derivative Manipulation for General Example Weighting
[article]
2020
arXiv
pre-print
Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach for training robust deep models under these adverse conditions. DM has two main merits. First, loss function and example weighting are common techniques in the literature. DM reveals their connection (a loss function does example weighting) and is a replacement of both. Second, despite that a loss defines an example
arXiv:1905.11233v10
fatcat:bu2wxnw4d5d6bdhngu53zocv64