18 citations · 21 across the 15 of their papers we have counts for
5 papers · 2 filters
Dual Representation Learning for Out-of-Distribution Detection
Zhilin Zhao, Longbing Cao
To classify in-distribution samples, deep neural networks explore strongly label-related information and discard weakly label-related information according to the information bottl…
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…
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…
Gray Learning from Non-IID Data with Out-of-distribution Samples
Zhilin Zhao, Longbing Cao, Chang-Dong Wang
The integrity of training data, even when annotated by experts, is far from guaranteed, especially for non-IID datasets comprising both in- and out-of-distribution samples. In an i…
Mixture of Online and Offline Experts for Non-stationary Time Series
Zhilin Zhao, Longbing Cao, Yuanyu Wan
We consider a general and realistic scenario involving non-stationary time series, consisting of several offline intervals with different distributions within a fixed offline time…