Abstract
We have been building the competitively growing neural network using temporal coding for quick one-shot object learning and glance object recognition, which is the core of our saliency-based scene memory model. This neural network represents objects using latency-based temporal coding and grows size and recognizability through learning and self-organization. This paper shows that self-organized learning is quickly performed and glance recognition is successfully performed by our model through simulation experiments of a robot equipped with a camera.
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© 2004 Springer-Verlag Berlin Heidelberg
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Atsumi, M. (2004). Scene Memory on Competitively Growing Neural Network Using Temporal Coding: Self-organized Learning and Glance Recognizability. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_60
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DOI: https://doi.org/10.1007/978-3-540-30499-9_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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