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Incremental learning involves processing continuous streams of information in real time without much incorporation of previous knowledge. It is crucial to humans and machine learning models. In this work, to alleviate the catastrophic forgetting characteristic of incremental learning in low-complexity models, we aim to combine the low complexity of single-task learning (STL) with the stability of multitask learning (MTL) through a two-stage similarity harmonizing mechanism (SHM). In the initialization stage, we construct an explainer by a multitask learning Shapley additive explanation (MTLSHAP) on a pretrained MTL model and return the model feature importance. In the incremental stage, the sample feature importance of incoming data computed by the explainer is compared with the model feature importance to establish disharmonies among samples. Hence, SHM impels the performance of an STL model to approach the designed MTL model. Numerical experiments show that the STL model improved by SHM is more stable while maintaining low complexity due to the acceptable additional cost of the initialization stage.

Improve the Performance and Stability of Incremental Learning by a Similarity Harmonizing Mechanism