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cs.LG2022
Out-of-distribution Detection by Cross-class Vicinity Distribution of In-distribution Data
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
Deep neural networks for image classification only learn to map in-distribution inputs to their corresponding ground truth labels in training without differentiating out-of-distrib…
cs.LG2022
Supervision Adaptation Balancing In-distribution Generalization and Out-of-distribution Detection
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
The discrepancy between in-distribution (ID) and out-of-distribution (OOD) samples can lead to \textit{distributional vulnerability} in deep neural networks, which can subsequently…
cs.LG2021
Revealing the Distributional Vulnerability of Discriminators by Implicit Generators
Zhilin Zhao, Longbing Cao, Kun-Yu Lin
In deep neural learning, a discriminator trained on in-distribution (ID) samples may make high-confidence predictions on out-of-distribution (OOD) samples. This triggers a signific…