Llopis-Ibor, L.Beltrán-Royo, CésarCuesta-Infante, A.Pantrigo, J.J.2024-09-162024-09-162023-02Laura Llopis-Ibor, Cesar Beltran-Royo, Alfredo Cuesta-Infante, Juan J. Pantrigo, Fast incremental learning by transfer learning and hierarchical sequencing, Expert Systems with Applications, Volume 212, 2023, 118580, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118580https://hdl.handle.net/10115/39553In 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 learningengAttribution-NonCommercial-NoDerivs 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/article10.1016/j.eswa.2022.118580info:eu-repo/semantics/embargoedAccess