Prototypical signatures enable generation of diverse negative samples for writer-independent offline signature verification, improving skilled forgery detection and allowing scalable linear SVM alternatives to RBF models.
Robust learning meets generative models: Can proxy distributions improve adversarial robustness?
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SAAD adaptively weights adversarial training samples by their transferability to the teacher, yielding higher AutoAttack robustness than prior distillation methods on CIFAR and Tiny-ImageNet without extra compute.
citing papers explorer
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A Prototypical Signature Approach for Writer-Independent Offline Signature Verification
Prototypical signatures enable generation of diverse negative samples for writer-independent offline signature verification, improving skilled forgery detection and allowing scalable linear SVM alternatives to RBF models.
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Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation
SAAD adaptively weights adversarial training samples by their transferability to the teacher, yielding higher AutoAttack robustness than prior distillation methods on CIFAR and Tiny-ImageNet without extra compute.