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Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks
We evaluate whether the method increases semantic alignment by evaluating model performance on adversarially perturbed data, with the idea that it should be easier for an adversary to switch one class to a similarly represented class.
Addressing Mistake Severity in Neural Networks with Semantic Knowledge
Most robust training techniques aim to improve model accuracy on perturbed inputs; as an alternate form of robustness, we aim to reduce the severity of mistakes made by neural networks in challenging conditions.
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