Training energy-based single-layer Hopfield and oscillatory networks with unsupervised and supervised algorithms for image classification - Archive ouverte HAL Access content directly
Journal Articles Neural Computing and Applications Year : 2023

Training energy-based single-layer Hopfield and oscillatory networks with unsupervised and supervised algorithms for image classification

Aida Todri-Sanial

Abstract

This paper investigates how to solve image classification with Hopfield neural networks (HNNs) and oscillatory neural networks (ONNs). This is a first attempt to apply ONNs for image classification. State-of-the-art image classification networks are multi-layer models trained with supervised gradient back-propagation, which provide high-fidelity results but require high energy consumption and computational resources to be implemented. On the contrary, HNN and ONN networks are single-layer, requiring less computational resources, however, they necessitate some adaptation as they are not directly applicable for image classification. ONN is a novel brain-inspired computing paradigm that performs low-power computation and is attractive for edge artificial intelligence applications, such as image classification. In this paper, we perform image classification with HNN and ONN by exploiting their auto-associative memory (AAM) properties. We evaluate precision of HNN and ONN trained with state-of-the-art unsupervised learning algorithms. Additionally, we adapt the supervised equilibrium propagation (EP) algorithm to single-layer AAM architectures, proposing the AAM-EP. We test and validate HNN and ONN classification on images of handwritten digits using a simplified MNIST set. We find that using unsupervised learning, HNN reaches 65.2%, and ONN 59.1% precision. Moreover, we show that AAM-EP can increase HNN and ONN precision up to 67.04% for HNN and 62.6% for ONN. While intrinsically HNN and ONN are not meant for classification tasks, to the best of our knowledge, these are the best-reported precisions of HNN and ONN performing classification of images of handwritten digits.
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Dates and versions

hal-04125593 , version 1 (18-10-2023)

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Madeleine Abernot, Aida Todri-Sanial. Training energy-based single-layer Hopfield and oscillatory networks with unsupervised and supervised algorithms for image classification. Neural Computing and Applications, 2023, 35, pp.18505-18518. ⟨10.1007/s00521-023-08672-0⟩. ⟨hal-04125593⟩
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