Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks. (arXiv:2211.00269v1 [cs.LG])

Adversarial training (AT) with imperfect supervision is significant but
receives limited attention. To push AT towards more practical scenarios, we
explore a brand new yet challenging setting, i.e., AT with complementary labels
(CLs), which specify a class that a data sample does not belong to. However,
the direct combination of AT with existing methods for CLs results in
consistent failure, but not on a simple baseline of two-stage training. In this
paper, we further explore the phenomenon and identify the …

adversarial attacks training