Locally-Connected, Irregular Deep Neural Networks For Biomimetic Active Vision In A Simulated Human
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)(2020)
Abstract
An advanced simulation framework has recently been introduced for exploring human perception and visuomotor control. In this context, we investigate locally-connected, irregular deep neural networks (liNets) for biomimetic active vision. Like commonly used CNNs, liNets are locally-connected, forming receptive fields, but unlike CNNs, they are suitable for spatially irregular photoreceptor distributions inspired by those found in foveated biological retinas. Compared to fully-connected deep neural networks, liNets accommodate a much greater number of retinal photoreceptors to enhance visual acuity without intractable memory consumption. LiNets serve well in the biomimetic active vision system embodied in a simulated human that learns active visuomotor control and active appearance-based recognition.
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Key words
active visuomotor control,active appearance-based recognition,irregular deep neural networks,simulated human,advanced simulation framework,human perception,liNets,CNNs,receptive fields,spatially irregular photoreceptor distributions,foveated biological retinas,LiNets,biomimetic active vision system
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