For Better Deep Neural Network Vision, Just Add Feedback (Loops)

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there’s a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for artificial intelligence developers, such as those improving self-driving car navigation.

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  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. READ MORE ON: MIT NEWS

Artistic rendering of artificial intelligence and deep learning visual recognition systems  Image: Christine Daniloff

Artistic rendering of artificial intelligence and deep learning visual recognition systems

Image: Christine Daniloff