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CVAM: continuous-valued associative memory for one-to-many associations

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Abstract

In this paper, we propose a CVAM (continuous-valued associative memory for one-to-many associations) with back-propagation learning and analyze the performance in detail. Conventional associative memories often deal with binary patterns, however, most of the data handled today are continuous-valued data. The basic architecture of the proposed CVAM is a three-layer perceptron with multiple sub-layers in the hidden layer. The multiple sub-layers enable one-to-many associations using back-propagation (BP) learning algorithm; each sub-layer memorizes single one-to-one association and the multiple sub-layers enables one-to-many associations. We carried out experiments to analyze the important properties such as memory capacity and noise tolerance performance using continuous-valued data. In addition, we conducted a demonstrative experiment to visually confirm the behavior of the proposed CVAM as an associative memory model using the CIFAR-10 image data set.

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  1. Faculty of Science and Technology, Keio University, Yokohama-shi, 223-8522, Japan

    Shunsuke Kano & Masafumi Hagiwara

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  1. Shunsuke Kano
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  2. Masafumi Hagiwara
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Correspondence to Masafumi Hagiwara.

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Kano, S., Hagiwara, M. CVAM: continuous-valued associative memory for one-to-many associations. Appl Intell 53, 5462–5472 (2023). https://doi.org/10.1007/s10489-022-03814-8

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  • DOI: https://doi.org/10.1007/s10489-022-03814-8

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