Compared to machines, humans are extremely good at
classifying images into categories, especially when they
possess prior knowledge of the categories at hand. If this
prior information is not available, supervision in the form
of teaching images is required. To learn categories more
quickly, people should see important and representative im-
ages first, followed by less important images later – or not at
all. 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, stu-
dents keep learning, so while image-importance depends on
their current knowledge, it also varies with time.
In this work we propose an Interactive Machine Teach-
ing algorithm that enables a computer to teach challeng-
ing visual concepts to a human. Our adaptive algorithm
chooses, online, which labeled images from a teaching set
should 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 us-
ing real human participants across several varied and chal-
lenging real-world datasets.