Abstract
In this paper, we present a simple yet powerful visual memory model called Increment Pattern Association Memory Model to achieve increment learning and rapid retrieval of visual information. We extend the basic pattern association network by learning patterns based on Leabra framework which combines error-driven learning and Hebbian learning together. In the proposed model, image features act as conditioned stimulus and class labels act as unconditioned stimulus. Increment Learning is achieved by assigning individual synaptic weight to different categories. Meanwhile, k-winners-take-all inhibitory competition function is employed to achieve sparse distributed representations. In addition, normalized dot product is used to realize rapid recall of visual memory. To evaluate the performance of the proposed model, we conduct a series of experiments on three benchmark datasets using three feature-extraction methods. Experimental results demonstrate that the proposed model has a compelling advantage over some prevalent neural networks such as SOINN, BPNN, Biased ART and Self-supervised ART.
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Acknowledgements
The paper is funded by the National Natural Science Foundation of P. R. China (No. 61671480) and the Natural Science Foundation of Shandong Provence (No. ZR2017MF069).
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Deng, L., Gao, M. & Wang, Y. Increment Learning and Rapid Retrieval of Visual Information Based on Pattern Association Memory. Neural Process Lett 48, 1597–1610 (2018). https://doi.org/10.1007/s11063-018-9789-5
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DOI: https://doi.org/10.1007/s11063-018-9789-5