Autonomous Learning: Unifying OOD Detection and Continual Learning
Bing Liu, Distinguished Professor and Peter L. and Deborah K. Wexler Professor of Computing at the University of Illinois Chicago
Abstract: Continual learning (CL) focuses on incrementally learning a sequence of tasks, with class incremental learning (CIL) being one of the most challenging settings. This talk begins by presenting a theoretical study of the CIL problem. The key result is that the necessary and sufficient conditions for effective CIL are strong within-task prediction and reliable out-of-distribution (OOD) detection. The theory unifies CIL and OOD detection, which are regarded as two completely different problems. Building on the theory, new CIL methods have been developed, which significantly outperform existing baselines. However, traditional CIL operates in a closed-world context. We then extend the theory to the open world—where unknown and out-of-distribution objects are encountered—leading to the learning paradigm of open-world CIL, or open-world continual learning (OWCL), enabling autonomous learning. In the last part of the talk, I will discuss challenges in OWCL and present a prototype system that learns on the fly continually and autonomously after deployment.
Cost: free