4 accepted papers
Object detection systems must reliably perceive objects of interest without being overly confident to ensure safe decision-making in dynamic environments. Filtering techniques based on out-of-distribution (OoD) detection are commonly added as an extra safeguard to filter hallucinations caused by ove
Out-of-distribution (OoD) detectors work alongside deep neural networks (DNNs) to reduce their risks in eliciting wrong predictions. Unfortunately, OoD detectors built on data-centric designs are also subject to robustness issues, as the DNNs. This paper examines the practical robustness of OoD dete…
Randomized smoothing (RS) is one of the prominent techniques to ensure the correctness of machine learning models, where point-wise robustness certificates can be derived analytically. While RS is well understood for classification, its application to generative models is unclear, since their output
Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-critical applications and interact with an open and dynamic environment. Nevertheless, integrating OoD detection into st…