@article{Ren_Stewart_Song_Kuleshov_Ermon_2018, title={Learning with Weak Supervision from Physics and Data-Driven Constraints}, volume={39}, url={https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2776}, DOI={10.1609/aimag.v39i1.2776}, abstractNote={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 prior knowledge — e.g. 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 amount of labels, or with no labels at all in some cases.}, number={1}, journal={AI Magazine}, author={Ren, Hongyu and Stewart, Russell and Song, Jiaming and Kuleshov, Volodymyr and Ermon, Stefano}, year={2018}, month={Mar.}, pages={27-38} }