Related papers: Taxonomic Class Incremental Learning

Taxonomic Class Incremental Learning

URL: http://arxiv.org/abs/2304.05547v1
Date: Wed, 12 Apr 2023 00:43:30 GMT
Title: Taxonomic Class Incremental Learning
Authors: Yuzhao Chen, Zonghuan Li, Zhiyuan Hu, Nuno Vasconcelos
Abstract summary: We propose the Taxonomic Class Incremental Learning problem. We unify existing approaches to CIL and taxonomic learning as parameter inheritance schemes. Experiments on CIFAR-100 and ImageNet-100 show the effectiveness of the proposed TCIL method.
Score: 57.08545061888821
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: The problem of continual learning has attracted rising attention in recent years. However, few works have questioned the commonly used learning setup, based on a task curriculum of random class. This differs significantly from human continual learning, which is guided by taxonomic curricula. In this work, we propose the Taxonomic Class Incremental Learning (TCIL) problem. In TCIL, the task sequence is organized based on a taxonomic class tree. We unify existing approaches to CIL and taxonomic learning as parameter inheritance schemes and introduce a new such scheme for the TCIL learning. This enables the incremental transfer of knowledge from ancestor to descendant class of a class taxonomy through parameter inheritance. Experiments on CIFAR-100 and ImageNet-100 show the effectiveness of the proposed TCIL method, which outperforms existing SOTA methods by 2% in terms of final accuracy on CIFAR-100 and 3% on ImageNet-100.

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