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When humans perform a task, such as playing a game, they selectively payattention to certain parts of the visual input, gathering relevant informationand sequentially combining it to build a representation from the sensory data.In this work, we explore leveraging where humans look in an image as animplicit indication of what is salient for decision making. We build on top ofthe UNREAL architecture in DeepMind Lab's 3D navigation maze environment. Wetrain the agent both with original images and foveated images, which weregenerated by overlaying the original images with saliency maps generated usinga real-time spectral residual technique. We investigate the effectiveness ofthis approach in transfer learning by measuring performance in the context ofnoise in the environment.

Attend Before you Act: Leveraging human visual attention for continual learning