45 citations · 89 across the 6 of their papers we have counts for
8 papers
XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection
Sunsheng Gu, Vahdat Abdelzad, Krzysztof Czarnecki
Explainable AI (XAI) methods are frequently applied to obtain qualitative insights about deep models' predictions. However, such insights need to be interpreted by a human observer…
Out-of-Distribution Detection for LiDAR-based 3D Object Detection
Chengjie Huang, Van Duong Nguyen, Vahdat Abdelzad +5
3D object detection is an essential part of automated driving, and deep neural networks (DNNs) have achieved state-of-the-art performance for this task. However, deep models are no…
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…