74 citations · 190 across the 12 of their papers we have counts for
3 papers · 1 filter
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…