Robust Semantic Segmentation by Redundant Networks With a Layer-Specific Loss Contribution and Majority Vote
Andreas Bär, Marvin Klingner, Serin Varghese, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
Abstract
The lack of robustness shown by deep neural networks (DNNs) questions their deployment in safety-critical tasks, such as autonomous driving. We pick up the recently introduced redundant teacher-student frameworks (3 DNNs) and propose in this work a novel error detection and correction scheme with application to semantic segmentation. It obtains its robustnesss by an online-adapted and therefore hard-to-attack student DNN during vehicle operation, which builds upon a novel layer-dependent inverse feature matching (IFM) loss. We conduct experiments on the Cityscapes dataset showing that this loss renders the adaptive student to be more than 20% absolute mean intersection-over-union (mIoU) better than in previous works. Moreover, the entire error correction virtually always delivers the performance of the best non-attacked network, resulting in an mIoU of about 50% even under strongest attacks (instead of 1...2%), while keeping the performance on clean data at about original level (ca. 75.7%).