Reliable Open-World Learning Against Out-of-distribution Data
The real world is open and full of unknowns, presenting significant challenges for AI systems that must reliably handle diverse, and sometimes anomalous inputs. Out-of-distribution (OOD) uncertainty arises when a machine learning model sees a test-time input that differs from its training data, and thus should not be predicted by …