Journal article Open Access

Continual learning for recurrent neural networks: An empirical evaluation

Andrea Cossu; Antonio Carta; Vincenzo Lomonaco; Davide Bacciu

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      <creatorName>Andrea Cossu</creatorName>
      <affiliation>University of Pisa</affiliation>
      <creatorName>Antonio Carta</creatorName>
      <affiliation>University of Pisa</affiliation>
      <creatorName>Vincenzo Lomonaco</creatorName>
      <affiliation>University of Pisa</affiliation>
      <creatorName>Davide Bacciu</creatorName>
      <affiliation>University of Pisa</affiliation>
    <title>Continual learning for recurrent neural networks: An empirical evaluation</title>
    <subject>continual learning; recurrent neural networks</subject>
    <date dateType="Issued">2021-08-05</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.neunet.2021.07.021</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Learning continuously during all model lifetime is fundamental to deploy &lt;a href=""&gt;machine learning&lt;/a&gt; solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with &lt;a href=""&gt;recurrent neural networks&lt;/a&gt; could pave the way to a large number of applications where incoming data is non stationary, like &lt;a href=""&gt;natural language processing&lt;/a&gt; and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications.&lt;/p&gt;

&lt;p&gt;We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/871385/">871385</awardNumber>
      <awardTitle>A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence</awardTitle>
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