Team

Daphne Auer, Niklas Penzel, Paul Bodesheim

Motivation

Lifelong learning denotes a concept for continuous learning from data streams that typically become available during the application of a recognition system, e.g., additional images captured during a visual monitoring study. Instead of only applying a fixed model pre-trained on an initial dataset, lifelong learning aims at leveraging the additional data collected during an application in order to improve the recognition model over time. It incorporates active learning to exploit human feedback via annotations of unlabeled samples that have an impact on model training. Furthermore, incremental learning is required to update model parameters effectively and efficiently. In unconstrained environments and open-world scenarios, novelty detection also needs to be considered.