Generative pseudorehearsal strategy for fault classification under an incremental learning

Subin Lee, Jun Geol Baek

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    As fault classification becomes more important in manufacturing industry, the state-of-art machine learning methods have been utilized. However, owing to the problem called catastrophic forgetting, the networks tend to forget the former knowledge. Thus, it is evident that overall classification performance has fallen, when training the existing model with new classes. We propose classification model that retains previous information using generative pseudorehearsal networks. In this method, newly arrived fault classes could be trained on same network which is parameterized by former data. The proposed method shows significant experimental results comparing to non-incremental methods, while achieving memory efficiency and solving the class imbalance problem.

    Original languageEnglish
    Title of host publicationProceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
    EditorsMeikang Qiu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages138-140
    Number of pages3
    ISBN (Electronic)9781728116631
    DOIs
    Publication statusPublished - 2019 Aug
    Event22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019 - New York, United States
    Duration: 2019 Aug 12019 Aug 3

    Publication series

    NameProceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019

    Conference

    Conference22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019
    Country/TerritoryUnited States
    CityNew York
    Period19/8/119/8/3

    Bibliographical note

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • Fault diagnostics and classification
    • Generative networks
    • Incremental learning
    • Pseudorehearsal strategy

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Computer Vision and Pattern Recognition
    • Hardware and Architecture
    • Human-Computer Interaction
    • Artificial Intelligence

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