Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders

• URL: http://arxiv.org/abs/2101.11391v1
• Date: Wed, 27 Jan 2021 13:40:16 GMT
• Title: Self-Calibrating Active Binocular Vision via Active Efficient Coding with Deep Autoencoders
• Authors: Charles Wilmot, Bertram E. Shi, Jochen Triesch
• Abstract summary: We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model follows the principle of Active Efficient Coding (AEC), a recent extension of the classic Efficient Coding Hypothesis to active perception.
• Score: 5.653716495767271
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a model of the self-calibration of active binocular vision comprising the simultaneous learning of visual representations, vergence, and pursuit eye movements. The model follows the principle of Active Efficient Coding (AEC), a recent extension of the classic Efficient Coding Hypothesis to active perception. In contrast to previous AEC models, the present model uses deep autoencoders to learn sensory representations. We also propose a new formulation of the intrinsic motivation signal that guides the learning of behavior. We demonstrate the performance of the model in simulations.

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