Deep Active Learning with Range Feedback for Facial Age Estimation

Deep learning has achieved unprecedented breakthroughs in machine learning research. However, the paucity of labeled training data, together with the human effort and time associated with obtaining a large amount of labeled data, poses significant challenges in training a reliable deep learning model. Active Learning (AL) algorithms automatically select the exemplar instances from large amounts of unlabeled data, and are instrumental in reducing the human annotation effort in inducing a machine learning model. However, in certain applications providing the exact label to queried unlabeled instances may be challenging even for human annotators. Vision based facial age estimation is one such application where it is difficult to estimate the exact age of a person merely from a face image; it maybe easier, and more practical, to provide other forms of annotation such as the best estimated lower and upper bounds on the age of the person within a given span. In this paper, we propose DALRange, a novel deep active learning framework, where annotators merely need to provide an estimated range on the label of an unlabeled sample, rather than the exact label. We formulate a loss function relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the network. To the best of our knowledge, this is the first research effort to develop an active learning algorithm to train a deep neural network, which poses only range label queries to the oracles. Our extensive empirical studies on human-annotated data corroborate the practical usefulness of our framework in applications where providing the exact labels to queried samples can be challenging.