Toward predictive machine learning for active vision
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We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective. Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies under a POMDP perspective. Computer simulations illustrate the effectiveness of the approach through a foveated inspection the input data. The pros and cons of the control policy are reviewed in details, showing interesting promises in term of processing compression, but also putative risks of a confirmation bias that may degrade the recognition performance if the model is too optimistic about its own predictions. The presented formalism is fully compliant with the auto-encoding framework and would deserve further developments under variational encoding architectures.
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