CIF: RI: Medium: Design principles and theory for data augmentation

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Generalization, or the ability to transfer knowledge from one context to the next, is a hallmark of human intelligence. In artificial intelligence (AI), however, models trained in one setting often fail when tested in a new setting, even if the shift is minor or imperceptible. To build more generalizable AI, most modern methods employ some form of data augmentation (DA), which applies transformations to the data to create virtual samples that are then added to the dataset. The resulting synthesis of new examples appears to build helpful properties in AI such as invariance or resistance to change to certain natural transformations, and robustness to new tasks as well as noise in existing tasks. Despite the promise and performance of DA procedures, they are mostly applied in an ad-hoc manner and need to be designed and tested on a dataset by dataset basis. A set of fundamental principles and theory to understand DA and its impact on model training and testing is lacking. To address this outstanding challenge, the investigators will provide a precise understanding of the impact of DA on generalization, and leverage this understanding to design novel augmentations that can be used across multiple applications and domains. In this project, the investigators propose a principled mathematical framework to 1) understand when DA helps and when DA could potentially hurt learning, 2) understand the structure induced by DA and characterize what makes high-quality augmentations, and 3) provide novel, systematic, and scalable design principles for augmenting data in new domains where we lack prior knowledge to guide us. These design principles will significantly broaden the applicability and promise of DA from computer vision to new domains (e.g., neural data, graphs and tabular data) where principled augmentations are still not known. Of special focus in this project will be applications of DA to neural activity, where augmentations have shown promise in building a more generalizable link between the brain and behavior. This research will also yield prescriptions for the role of DA in advancing fairness, accountability and transparency in modern machine learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.