Previous work assigned debaters/consultants an answer to argue for.

Despite the rich potential for innovation, the learning process of algorithms under real-world conditions is a challenge [1, 38, 46].

Infants possess a remarkable ability to rapidly learn and process visual inputs.

In this work we study the problem of actively learning binary classifiers from a given concept class, i.e., learning by utilizing unlabeled data and submitting targeted queries about their labels to a domain expert. We evaluate the quality of our solutions by considering the learning curves they induce, i.e., the rate of