74 citations · 136 across the 4 of their papers we have counts for
4 papers
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…
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…
Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance
Taylor Denouden, Rick Salay, Krzysztof Czarnecki +3
There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems. A number of…
Calibrating Uncertainties in Object Localization Task
Buu Phan, Rick Salay, Krzysztof Czarnecki +3
In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help suppor…