Separating inference from feature learning in deep unsupervised visual saliency estimation

Abstract

Robotic agents, when not equipped with traditional means to capture information about their surroundings, must autonomously learn to extract this information from a very complex environment. In the context of developmental robotics, we use unsupervised representation learning, and more specifically deep autoencoders, in order to capture visual representations. These generic visual representations can in turn be used by different modules of a cognitive architecture. In this paper, we propose a model using these representations to learn an intrinsic measure of bottom-up visual saliency which could later be implemented into a mobile robot.