For better deep neural network vision, just add feedback (loops)

While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child’s play for primates such as humans.

In findings published in Nature Neuroscience, McGovern Institute investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Deep convolutional neural networks are currently the most successful models for accurately recognizing objects on a fast timescale and have a general architecture inspired by the primate ventral visual stream, cortical regions that progressively build an accessible and refined representation of viewed objects.

Rather than trying to guess why deep learning was having problems recognizing an object, the authors took an unbiased approach that turned out to be critical.

Instead, the authors presented the deep learning system, as well as monkeys and humans, with images, homing in on “Challenge images” where the primates could easily recognize the objects in those images, but a feedforward DCNN ran into problems.

“What the computer vision community has recently achieved by stacking more and more layers onto artificial neural networks, evolution has achieved through a brain architecture with recurrent connections,” says Kar.

“Since entirely feedforward deep convolutional nets are now remarkably good at predicting primate brain activity, it raised questions about the role of feedback connections in the primate brain. This study shows that, yes, feedback connections are very likely playing a role in object recognition after all.”

This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.

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