What Can the Primate Brain Teach Us about Robust Object Recognition?

Many of the current state-of-the-art object recognition models such as convolutional neural networks (CNN) are loosely inspired by the primate visual system. However, there still exist many discrepancies between these models and primates, both in terms of their internal processing mechanisms and their respective behavior on object recognition tasks. Of particular concern, many current models suffer from remarkable sensitivity to adversarial attacks, a phenomenon which does not appear to plague the primate visual system. Recent work has demonstrated that adding more biologically-inspired components or otherwise driving models to use representations more similar to the primate brain is one way to improve their robustness to adversarial attacks. In this talk, I will review some of these insights and successes such as relationships between the primary visual cortex and robustness, discuss recent findings about how neural recordings from later regions of the primate ventral stream might help to align model and human behavior, and finally conclude with recent neurophysiological results questioning exactly how robust representations in the primate brain truly are.

Bio: Joel Dapello is a PhD candidate in Applied Math at the Harvard School of Engineering and Applied Sciences, currently working with Jim DiCarlo and David Cox at the intersection of machine learning and primate cognitive neuroscience. Prior to this, Joel was the founding engineer at BioBright, and received his bachelors in neuroscience from Hampshire College. Joel’s interests are centered around neural computation and information processing in both biological and artificial neural systems.