arXiv cs.CV
· Papers
SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks
arXiv:2608.10289v1 Announce Type: new Abstract: Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-represented scenarios. This necessitates the need to evaluate the semantic robustness of perception models; conformance o