67 citations · 432 across the 55 of their papers we have counts for
7 papers · 1 filter
Out-of-distribution Detection with Implicit Outlier Transformation
Qizhou Wang, Junjie Ye, Feng Liu +5
Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typi…
Is Out-of-Distribution Detection Learnable?
Zhen Fang, Yixuan Li, Jie Lu +3
Supervised learning aims to train a classifier under the assumption that training and test data are from the same distribution. To ease the above assumption, researchers have studi…
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
Xiong Peng, Feng Liu, Jingfen Zhang +4
Through using only a well-trained classifier, model-inversion (MI) attacks can recover the data used for training the classifier, leading to the privacy leakage of the training dat…
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack
Ruize Gao, Jiongxiao Wang, Kaiwen Zhou +5
The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational co…
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms
Guangzhi Ma, Jie Lu, Feng Liu +2
The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on t…
Learning Bounds for Open-Set Learning
Zhen Fang, Jie Lu, Anjin Liu +2
Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. In this paper, we target a more chal…