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Deep Active Autoencoders for Outlier Detection

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Abstract

The variety and lack of labels for outliers make it difficult to establish a fixed outlier model, which facilitates unsupervised algorithms for mainstream outlier detection. However, unsupervised methods rely on the assumption that there are no outliers within the dataset or that the outliers are sporadically distributed, thus leading to unsatisfactory detection accuracy within large-scale, high-dimensional datasets, especially image datasets. In view of this, the present study proposes a novel outlier detection method, called Active Autoencoder (AAE), which can be used to break through the bottleneck of unsupervised learning. AAE improves the performance of autoencoder through use of influence-based active learning in combination with a novel way to change sample weights by expansion-shrinkage operator. Experiments on benchmark and fundus image datasets demonstrate that the proposed method achieves superior performance compared to alternatives.

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Acknowledgements

This work was supported partially by the Key-Area Research and Development Program of Guangdong Province (No. 2019B) and Sichuan Science and Technology Program (Nos. 2019YJ0176, 2019YJ0177, 2019YFQ0005). The authors also wish to thank the anonymous reviewers for their thorough review and highly appreciate their useful comments and suggestions.

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Authors and Affiliations

  1. Chengdu University of Technology, Chengdu, China

    Jin Ning

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Leiting Chen, Chuan Zhou & Yang Wen

  3. Digital Media Technology Key Laboratory of Sichuan Province, Chengdu, China

    Leiting Chen, Chuan Zhou & Yang Wen

  4. Institute of Electronic and Information Engineering in Guangdong, University of Electronic Science and Technology of China, Dongguan, China

    Leiting Chen, Chuan Zhou & Yang Wen

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  1. Jin Ning
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  2. Leiting Chen
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  3. Chuan Zhou
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  4. Yang Wen
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Correspondence to Chuan Zhou.

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Ning, J., Chen, L., Zhou, C. et al. Deep Active Autoencoders for Outlier Detection. Neural Process Lett 54, 1399–1411 (2022). https://doi.org/10.1007/s11063-021-10687-4

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