104 citations · 447 across the 17 of their papers we have counts for
9 papers · 1 filter
A Safety Assurable Human-Inspired Perception Architecture
Rick Salay, Krzysztof Czarnecki
Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the…
The missing link: Developing a safety case for perception components in automated driving
Rick Salay, Krzysztof Czarnecki, Hiroshi Kuwajima +6
Safety assurance is a central concern for the development and societal acceptance of automated driving (AD) systems. Perception is a key aspect of AD that relies heavily on Machine…
The Effect of Optimization Methods on the Robustness of Out-of-Distribution Detection Approaches
Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay
Deep neural networks (DNNs) have become the de facto learning mechanism in different domains. Their tendency to perform unreliably on out-of-distribution (OOD) inputs hinders their…
Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output
Vahdat Abdelzad, Krzysztof Czarnecki, Rick Salay +3
Deep neural networks achieve superior performance in challenging tasks such as image classification. However, deep classifiers tend to incorrectly classify out-of-distribution (OOD…
Out-of-distribution Detection in Classifiers via Generation
Sachin Vernekar, Ashish Gaurav, Vahdat Abdelzad +3
By design, discriminatively trained neural network classifiers produce reliable predictions only for in-distribution samples. For their real-world deployments, detecting out-of-dis…
Analysis of Confident-Classifiers for Out-of-distribution Detection
Sachin Vernekar, Ashish Gaurav, Taylor Denouden +4
Discriminatively trained neural classifiers can be trusted, only when the input data comes from the training distribution (in-distribution). Therefore, detecting out-of-distributio…