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How robust accuracy suffers from certified training with convex relaxations
In this paper, we systematically compare the standard and robust error of these two robust training paradigms across multiple computer vision tasks.
Margin-based sampling in high dimensions: When being active is less efficient than staying passive
It is widely believed that given the same labeling budget, active learning (AL) algorithms like margin-based active learning achieve better predictive performance than passive learning (PL), albeit at a higher computational cost.
Why adversarial training can hurt robust accuracy
Machine learning classifiers with high test accuracy often perform poorly under adversarial attacks.
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