Recognition as Navigation in Energy-Based Models

Abstract

Human vision has an exceptional ability to recognize complex signals from limited and ambiguous observations, which is believed to comprise lower-level processes generating possible explanations for the observations, and higher-level systems selecting the most plausible ones of them. There is a lack of comparable mechanisms in modern artificial intelligence visual recognition solutions that would enable an improved generalization and robustness. This thesis proposes and studies a novel brain-inspired algorithm for face recognition which tackles the problem from a new angle – recognition can be solved as a navigation problem in a space of latent representations. Further, we show that the steps of this navigation correspond to sensible images that the model "imagines" during the process of navigation, comparable to a human imagining possible explanations to the observations which he/she is trying to recognize as an object or a person. In addition to this, we present that with some parameter tuning the algorithm can improve the separability of correct and incorrect navigation trajectories – like the explanations proposed by lower-level processes in the brain – as Fisher's discriminant ratio by up to 0.14 which, according to our guess, corresponds to an increase in accuracy between 5-15%.