Online metric learning for an adaptation to confidence drift
Lydia J. Fischer, Barbara Hammer, Heiko Wersing · 2016
One of the main aims of lifelong learning architectures is to efficiently and reliably cope with the stability-plasticity dilemma. A viable solution of this dilemma combines a static offline classifier, which preserves ground knowledge that should be respected during training, with an incremental online learning of new or specific information encountered during use. A feasible realisation has been published lately based on intuitive distance-based classifiers using the concept of metric learning (Fischer et al.: Combining offline and online classifiers for life-long learning (OOL), IJCNN'15). One crucial aspect of such a system is how to combine the offline and online model. A generic approach, taken in OOL, uses a dynamic classifier selection strategy based on confidences of both classifiers. This can cause problems in the case of confidence drift, especially when the validity of the confidence estimation of the static offline classifier changes. This pitfall occurs in the context of metric learning whenever the metric tensor of the online system becomes orthogonal to the metric of the offline system, hence the respective internal data description mismatch. We propose an efficient metric learning strategy which allows an online adaptation of an invalid confidence estimation of the OOL architecture in case of confidence drift.