Adversarial training (AT) is one of the most effective strategies for
promoting model robustness, whereas even the state-of-the-art adversarially
trained models struggle to exceed 65% robust test accuracy on CIFAR-10 without
additional data, which is far from practical. A natural way to improve beyond
this accuracy bottleneck is to introduce a rejection option, where confidence
is a commonly used certainty proxy. However, the vanilla confidence can
overestimate the model certainty if the input is wrongly classified. To this
end, we propose to use true confidence (T-Con) (i.e., predicted probability of
the true class) as a certainty oracle, and learn to predict T-Con by rectifying
confidence. Intriguingly, we prove that under mild conditions, a rectified
confidence (R-Con) rejector and a confidence rejector can be coupled to
distinguish any wrongly classified input from correctly classified ones. We
also quantify that training R-Con to be aligned with T-Con could be an easier
task than learning robust classifiers. In our experiments, we evaluate our
rectified rejection (RR) module on CIFAR-10, CIFAR-10-C, and CIFAR-100 under
several attacks, and demonstrate that the RR module is well compatible with
different AT frameworks on improving robustness, with little extra computation.

360 Mobile Vision - 360mobilevision.com North & South Carolina Security products and Systems Installations for Commercial and Residential - $55 Hourly Rate. ACCESS CONTROL, INTRUSION ALARM, ACCESS CONTROLLED GATES, INTERCOMS AND CCTV INSTALL OR REPAIR 360 Mobile Vision - 360mobilevision.com is committed to excellence in every aspect of our business. We uphold a standard of integrity bound by fairness, honesty and personal responsibility. Our distinction is the quality of service we bring to our customers. Accurate knowledge of our trade combined with ability is what makes us true professionals. Above all, we are watchful of our customers interests, and make their concerns the basis of our business.

By admin