Learning with weak supervision from physics and data-driven constraints

Abstract

In many applications of machine learning, labeled data is scarce and obtaining additional labels is expensive. We introduce a new approach to supervising learning algorithms without labels by enforcing a small number of domain-specific constraints over the algorithms' outputs. The constraints can be provided explicitly based on pri- or knowledge ' for example, we may require that objects detected in videos satisfy the laws of physics ' or implicitly extracted from data using a novel framework inspired by adversarial training. We demonstrate the effectiveness of constraint-based learning on a variety of tasks ' including tracking, object detection, and human pose estimation ' and we find that algorithms supervised with constraints achieve high accuracies with only a small number of labels, or with no labels at all in some cases.