Brains, Minds + Machines Seminar Series: Feedforward and feedback processes in visual recognition

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November 5, 2019 - 4:00 pm to 5:00 pm
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Thomas Serre, Cognitive, Linguistic & Psychological Sciences Department, Carney Institute for Brain Science, Brown University


Title: Feedforward and feedback processes in visual recognition


Abstract: Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive fields that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture which addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.​


November 5, 2019
4:00 pm to 5:00 pm
Singleton Auditorium

43 Vassar Street, Cambridge MA 02139