Label-Assisted Memory Autoencoder for Unsupervised Out-of-Distribution Detection

Shuyi ZHANG, Chao PAN, Liyan SONG, Xiaoyu WU, Zheng HU, Ke PEI, Peter TINO, Xin YAO*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

5 Citations (Scopus)


Out-of-Distribution (OoD) detectors based on AutoEncoder (AE) rely on an underlying assumption that an AE network cannot reconstruct OoD data as good as in-distribution (ID) data when it is constructed based on ID data only. However, this assumption may be violated in practice, resulting in a degradation in detection performance. Therefore, alleviating the factors violating this assumption can potentially improve the robustness of OoD performance. Our empirical studies also show that image complexity can be another factor hindering detection performance for AE-based detectors. To cater for these issues, we propose two OoD detectors LAMAE and LAMAE+. Both can be trained without the availability of any OoD-related data. The key idea is to regularize the AE network architecture with a classifier and a label-assisted memory to confine the reconstruction of OoD data while retaining the reconstruction ability for ID data. We also adjust the reconstruction error by taking image complexity into consideration. Experimental studies show that the proposed OoD detectors can perform well on a wider range of OoD scenarios. © 2021, Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases. Research Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part III
EditorsNuria OLIVER, Fernando PÉREZ-CRUZ, Stefan KRAMER, Jesse READ, Jose A. LOZANO
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Electronic)9783030865238
ISBN (Print)9783030865221
Publication statusPublished - 2021
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021 - Bilbao, Spain
Duration: 13 Sept 202117 Sept 2021

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameLecture Notes in Artificial Intelligence
ISSN (Print)2945-9133
ISSN (Electronic)2945-9141

Public Lecture

Public LectureEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021
Abbreviated titleECML PKDD 2021

Bibliographical note

This work was supported by National Natural Science Foundation of China (Grant No. 62002148), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), Shenzhen Fundamental Research Program (Grant No. JCYJ20190809121403553), Research Institute of Trustworthy Autonomous Systems, and Huawei.


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