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Computer Vision That Works More Like a Brain Sees More Like People Do

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When deep Learning computer vision systems establish efficient ways to solve visual problems, they end up with artificial circuits that work similarly to the neural circuits that process visual information in our own brains.

Researchers made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition.

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James DiCarlo and colleagues at the Massachusetts Institute of Technology trained an artificial neural network to function more like the human and primate brain's inferior temporal (IT) cortex to improve computer vision.

The researchers constructed a computer vision model based on neural data from primate vision-processing neurons, and tasked it to recognize objects.

DiCarlo said this made the artificial neural circuits process visual information differently.

The researchers found the biologically informed model IT layer aligned better with the IT neural data than a similarly-sized network model that lacked neural-data training.

They also discovered the neurally aligned model was more resilient against adversarial attacks for assessing computer vision and artificial intelligence systems.

From MIT News
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