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Defenses in Adversarial Machine Learning: A Survey
Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks, describing that ML systems may produce inconsistent and incomprehensible predictions with humans at some particular cases.
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Specifically, we view the training set as an imbalanced distribution over its samples, which is transported by OT to a balanced distribution obtained from the meta set.
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