Fast incremental learning by transfer learning and hierarchical sequencing

Abstract

In this paper we address the Class Incremental Learning (CIL) problem, characterized by sequences of data batches in which examples of different classes occur at different times. From a theoretical point of view, we propose a new approach that we call hierarchical sequencing and prove that any CIL task can be sequenced into simple incremental classification tasks by means of the hierarchical sequencing. From a practical point of view, we propose the HILAND method for image classification, which combines the hierarchical sequencing with transfer learning. In our experiments, the HILAND method has obtained state-of-the-art results for the CIL problem, but with far less training effort through transfer learning.