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. Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data. The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression. Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.
This paper introduces a machine learning adaptation of the active inference framework proposed by Friston (2010), and applies it to the task of image classification on MNIST through a foveated inspection of images. It describes a cognitive architecture for the same, and provide analyses in terms of processing compression and "confirmation biases" in the model.
– Active perception, and more specifically recognition through saccades (or viewpoint selection) is an interesting biologically-inspired approach and seems like an intuitive and promising way to improve efficiency. The problem and its potential applications are well motivated.
– The perception-driven control formulation is well-detailed and simple to follow.
– The achieved compression rates are significant and impressive, though additional demonstration of performance on more challenging datasets would have been more compelling
Questions and comments:
– While an 85% compression rate is significant, 88% accuracy on MNIST seems poor. A plot demonstrating the tradeoff of 
accuracy for compression (by varying Href or other parameters) would provide a more complete picture of performance. Knowing baseline performance (without active inference) would help put numbers in perspective by providing a performance bound due to modeling choices.
– What does the distribution of number of saccades required per recognition (for a given threshold) look like over the entire dataset, i.e. how many are dead-easy vs difficult?
– Steady state assumption: How can this be relaxed to further generalize to non-static scenes?
– Figure 3 is low resolution and difficult to read.
Post-rebuttal comments:
I have revised my score after considering comments from other reviewers and the revised paper. While the revised version contains more experimental details, the paper in its present form lacks comparisons to other gaze selection and saliency models which are required to put results in context. The paper also contains grammatical errors and is somewhat difficult to understand. Finally, while it proposes an interesting formulation of a well-studied problem, more comparisons and analysis are required to validate the approach.