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Unsupervised Continual Learning via Self-adaptive Deep Clustering Approach

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Continual Semi-Supervised Learning (CSSL 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13418))

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Abstract

Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the problem of task boundaries and task IDs which must be known for model’s updates or model’s predictions hindering feasibility for real-time deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner, (KIERA), is proposed in this paper. KIERA is developed from the notion of flexible deep clustering approach possessing an elastic network structure to cope with changing environments in the timely manner. The centroid-based experience replay is put forward to overcome the catastrophic forgetting problem. KIERA does not exploit any labelled samples for model updates while featuring a task-agnostic merit. The advantage of KIERA has been numerically validated in popular continual learning problems where it shows highly competitive performance compared to state-of-the art approaches. Our implementation is available in https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/P9DFJH.

M. Pratama and A. Ashfahani share equal contributions. This work was carried out when M. Pratama was with SCSE, NTU, Singapore.

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Author information

Authors and Affiliations

  1. STEM, University of South Australia, Adelaide, Australia

    Mahardhika Pratama

  2. SCSE, Nanyang Technological University, Singapore, Singapore

    Andri Ashfahani

  3. DKBMS, Johanes Kepler University, Linz, Austria

    Edwin Lughofer

Authors
  1. Mahardhika Pratama
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  2. Andri Ashfahani
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  3. Edwin Lughofer
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Corresponding author

Correspondence to Mahardhika Pratama .

Editor information

Editors and Affiliations

  1. Oxford Brookes University, Oxford, UK

    Fabio Cuzzolin

  2. Huawei Technologies Canada Co., Ltd., Burnaby, BC, Canada

    Kevin Cannons

  3. University of Pisa, Pisa, Italy

    Vincenzo Lomonaco

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Pratama, M., Ashfahani, A., Lughofer, E. (2022). Unsupervised Continual Learning via Self-adaptive Deep Clustering Approach. In: Cuzzolin, F., Cannons, K., Lomonaco, V. (eds) Continual Semi-Supervised Learning. CSSL 2021. Lecture Notes in Computer Science(), vol 13418. Springer, Cham. https://doi.org/10.1007/978-3-031-17587-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-17587-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17586-2

  • Online ISBN: 978-3-031-17587-9

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