Using Human Perception to Inform Machine Perception

Modern machine learning has origins in human learning, taking cues from human perception to build, train and evaluate machine learning models. As machine learning (ML) has begun to outperform humans in many challenging tasks, the focus has shifted from modeling humans to simply improving performance of these ML models. Join Dr. Emily Hand, Associate Professor and Graduate Program Director in Computer Science and Engineering at the University of Nevada, Reno, and Director of the Machine Perception Lab, as she details approaches to explainable attribute recognition, prominent feature recognition and face recognition, and the influence of human perception.