Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

• URL: http://arxiv.org/abs/2310.20268v1
• Date: Tue, 31 Oct 2023 08:38:14 GMT
• Title: Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
• Authors: Fuyuan Hu, Jian Zhang, Fan Lyu, Linyan Li, Fenglei Xu
• Abstract summary: Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples. In this paper, we propose a Sample-to-Class (S2C) graph learning method for FSCIL.
• Score: 10.111587226277647
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot samples. In this paper, we promote the idea to the incremental scenario, and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Specifically, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both new and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning in the FSCIL scenario. Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process. The multi-stage training strategy is designed to build S2C graph from base to few-shot stages, and improve the capacity via an extra pseudo-incremental stage. Experiments on three popular benchmark datasets show that our method clearly outperforms the baselines and sets new state-of-the-art results in FSCIL.

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