Toward predictive machine learning for active vision


Nov 07, 2017 (modified: Nov 07, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: 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 te recognition performance if the model is too optimistic about is own predictions. The presented formalism is fully compliant with the auto-encoding framework and would deserve further developments under variational encoding architectures.
  • TL;DR: Pros and cons of saccade-based computer vision under a predictive coding perspective
  • Keywords: active inference, predictive coding, motor control