Conference paper Open Access

Training Convolutional Neural Networks with Competitive Hebbian Learning Approaches

Gabriele Lagani; Fabrizio Falchi; Claudio Gennaro; Giuseppe Amato


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        <foaf:name>Claudio Gennaro</foaf:name>
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        <foaf:name>Giuseppe Amato</foaf:name>
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    <dct:title>Training Convolutional Neural Networks with Competitive Hebbian Learning Approaches</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2022</dct:issued>
    <dcat:keyword>Neural networks, Machine learning, Hebbian learning Competitive learning, Computer vision,Biologically inspired</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2022-02-02</dct:issued>
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    <dct:description>&lt;p&gt;We explore competitive Hebbian learning strategies to train feature detectors in Convolutional Neural Networks (CNNs), without supervision. We consider variants of the Winner-Takes-All (WTA) strategy explored in previous works, i.e. k-WTA, e-soft-WTA and p-soft-WTA, performing experiments on different object recognition datasets. Results suggest that the Hebbian approaches are effective to train early feature extraction layers, or to re-train higher layers of a pre-trained network, with soft competition generally performing better than other Hebbian approaches explored in this work. Our findings encourage a path of cooperation between neuroscience and computer science towards a deeper investigation of biologically inspired learning principles.&lt;/p&gt;</dct:description>
    <dct:description>In Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science, vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_2</dct:description>
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