Statistics Seminar

Current deep neural networks for image recognition do not know when they don’t know. Non-task related images are assigned to one of the classes with high confidence. Thus the machine learning system cannot trigger human intervention or go over into to a safe state when it is applied out of its specification. I will discuss our recent work to tackle this problem via (certifiably) adversarially robust out-of-distribution detection and show the usage of (robust) out-of-distribution detection in open-world semi-supervised learning.