Abstract

Compared to machines, humans are extremely good at classifying images into categories, especially when theypossess prior knowledge of the categories at hand. If this prior information is not available, supervision in the formof teaching images is required. To learn categories morequickly, people should see important and representative im-ages first, followed by less important images later – or not atall. However, image-importance is individual-specific, i.e. a teaching image is important to a student if it changes their overall ability to discriminate between classes. Further, students keep learning, so while image-importance depends on their current knowledge, it also varies with time. In this work we propose an Interactive Machine Teaching algorithm that enables a computer to teach challenging visual concepts to a human. Our adaptive algorithmchooses, online, which labeled images from a teaching setshould be shown to the student as they learn. We show that a teaching strategy that probabilistically models the student’s ability and progress, based on their correct and incorrect answers, produces better ‘experts’. We present results using real human participants across several varied and challenging real-world datasets.