Deep neural networks that identify shapes nearly as well as humans

Deep neural networks (DNNs) are capable of learning to identify shapes, so "we're on the right track in developing machines with a visual system and vocabulary as flexible and versatile as ours," say KU Leuven researchers. "For the first time, a dramatic increase in performance has been observed on object and scene categorization tasks, quickly reaching performance levels rivaling humans," they note in an open-access paper in PLOS Computational Biology. Categorization accuracy for models created by three DNNs (CaffeNet, VGG-19, and GoggLeNet) for three types of images (color, grayscaled, silhouette). For each type, mean human performance is indicated by a gray horizontal line, with the gray surrounding band depicting 95% confidence intervals. Error bars (vertical black lines) depict 95% confidence intervals.