Having Second Thoughts? Let's hear it
Jung H. Lee Sujith Vijayan
Deep learning models loosely mimic bottom-up signal pathways from low-order
sensory areas to high-order cognitive areas. After training, DL models can
outperform humans on some domain-specific tasks, but their decision-making
process has been known to be easily disrupted. Since the human brain consists
of multiple functional areas highly connected to one another and relies on
intricate interplays between bottom-up and top-down (from high-order to
low-order areas) processing, we hypothesize that incorporating top-down signal
processing may make DL models more robust. To address this hypothesis, we
propose a certification process mimicking selective attention and test if it
could make DL models more robust. Our empirical evaluations suggest that this
newly proposed certification can improve DL models' accuracy and help us build
safety measures to alleviate their vulnerabilities with both artificial and
natural adversarial examples.