New research from MIT suggests that a certain type of computer vision model that is trained to be robust to imperceptible noise added to image data encodes visual representations similarly to the way humans do using peripheral vision. Researchers find similarities between how some computer-vision systems process images and how humans see out of the corners of our eyes. Perhaps computer vision and human vision have more in common than meets the eye? Research from MIT suggests that a certain type of robust computer-vision model perceives visual representations similarly to the way humans do using peripheral vision. These models, known as adversarially robust models, are designed to overcome subtle bits of noise that have been added to image data.

Perhaps computer vision and human vision have more in common than meets the eye? Research from MIT suggests that a certain type of robust computer-vision model perceives visual representations similarly to the way humans do using peripheral vision. These models, known as adversarially robust models, are designed to overcome subtle bits of noise that have been added to image data. The way these models learn to transform images is similar to some elements involved in human peripheral processing, the researchers found. But because machines do not have a visual periphery, little work on computer vision models has focused on peripheral processing, says senior author Arturo Deza, a postdoc in the Center for Brains, Minds, and Machines.