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Continually trained life-long classification

  • S.I. : WSOM 2019
  • Published:
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Abstract

Two challenges can be found in a life-long classifier that learns continually: the concept drift, when the probability distribution of data is changing in time, and catastrophic forgetting when the earlier learned knowledge is lost. There are many proposed solutions to each challenge, but very little research is done to solve both challenges simultaneously. We show that both the concept drift and catastrophic forgetting are closely related to our proposed description of the life-long continual classification. We describe the process of continual learning as a wrap modification, where a wrap is a manifold that can be trained to cover or uncover a given set of samples. The notion of wraps and their cover/uncover modifiers are theoretical building blocks of a novel general life-long learning scheme, implemented as an ensemble of variational autoencoders. The proposed algorithm is examined on evaluation scenarios for continual learning and compared to state-of-the-art algorithms demonstrating the robustness to catastrophic forgetting and adaptability to concept drift but also showing the new challenges of the life-long classification.

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Notes

  1. Implementation of the evaluation framework and the ensgendel algorithm is available at https://github.com/comrob/ensgendel.

  2. https://github.com/giparisi/GDM.

  3. https://github.com/fikavw/CloGAN

  4. Non-intuitively, the famous Peano space-filling curve shows that there exists a continuous function that maps a continuous curve to a two-dimensional square that can be generalized to the n-dimensional cubes.

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Acknowledgements

This work is an extension of the paper presented at the 13th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM 2019), where it received the Best Student Paper award.

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  1. Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27, Prague 6, Czech Republic

    Rudolf Szadkowski, Jan Drchal & Jan Faigl

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  1. Rudolf Szadkowski
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Correspondence to Rudolf Szadkowski.

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This work was supported by the Czech Science Foundation (GAČR) under research project No. 18-18858S.

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Szadkowski, R., Drchal, J. & Faigl, J. Continually trained life-long classification. Neural Comput & Applic 34, 135–152 (2022). https://doi.org/10.1007/s00521-021-06154-9

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