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Transfer Learning with Sparse Associative Memories

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Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation (ICANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11727))

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Abstract

In this paper, we introduce a novel layer designed to be used as the output of pre-trained neural networks in the context of classification. Based on Associative Memories, this layer can help design deep neural networks which support incremental learning and that can be (partially) trained in real time on embedded devices. Experiments on the ImageNet dataset and other different domain specific datasets show that it is possible to design more flexible and faster-to-train Neural Networks at the cost of a slight decrease in accuracy.

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Author information

Authors and Affiliations

  1. IMT Atlantique, Technopole Brest Iroise, 29238, Brest, France

    Quentin Jodelet & Vincent Gripon

  2. Keio University, Yagami Campus, Yokohama, 223-8522, Japan

    Quentin Jodelet & Masafumi Hagiwara

Authors
  1. Quentin Jodelet
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  2. Vincent Gripon
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  3. Masafumi Hagiwara
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Corresponding author

Correspondence to Quentin Jodelet .

Editor information

Editors and Affiliations

  1. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Igor V. Tetko

  2. Institute of Computer Science, Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  3. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Pavel Karpov

  4. Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany

    Fabian Theis

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Jodelet, Q., Gripon, V., Hagiwara, M. (2019). Transfer Learning with Sparse Associative Memories. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019. Lecture Notes in Computer Science(), vol 11727. Springer, Cham. https://doi.org/10.1007/978-3-030-30487-4_39

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  • DOI: https://doi.org/10.1007/978-3-030-30487-4_39

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