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We develop a comprehensive description of the active inference framework, asproposed by Friston (2010), under a machine-learning compliant perspective.Stemming from a biological inspiration and the auto-encoding principles, thesketch of a cognitive architecture is proposed that should provide ways toimplement estimation-oriented control policies. Computer simulations illustratethe effectiveness of the approach through a foveated inspection of the inputdata. The pros and cons of the control policy are analyzed in detail, showinginteresting promises in terms of processing compression. Though optimizingfuture posterior entropy over the actions set is shown enough to attain locallyoptimal action selection, offline calculation using class-specific saliencymaps is shown better for it saves processing costs through saccades pathwayspre-processing, with a negligible effect on the recognition/compression rates.

Toward predictive machine learning for active vision