Application Feedback in Guiding a Deep-Layered Perception Model

8 years 6 months ago
Application Feedback in Guiding a Deep-Layered Perception Model
Deep-layer machine learning architectures continue to emerge as a promising biologically-inspired framework for achieving scalable perception in artificial agents. State inference is a consequence of robust perception, allowing the agent to interpret the environment with which it interacts and map such interpretation to desirable actions. However, in existing deep learning schemes, the perception process is guided purely by spatial regularities in the observations, with no feedback provided from the target application (e.g. classification, control). In this paper, we propose a simple yet powerful feedback mechanism, based on adjusting the sample presentation distribution, which guides the perception model in allocating resources for patterns observed. As a result, a much more focused state inference can be achieved leading to greater accuracy and overall performance. The proposed paradigm is demonstrated on a small-scale yet complex image recognition task, clearly illustrating the adva...
Itamar Arel, Shay Berant
Added 12 May 2011
Updated 12 May 2011
Type Journal
Year 2010
Where BICA
Authors Itamar Arel, Shay Berant
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