TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning

Shuangmei Wang; Yang Cao; Tieru Wu

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arXiv · 2312.04225

TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning

Shuangmei Wang·Yang Cao·Tieru Wu

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Abstract

Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of old classes or overfitting to new classes. We propose TLCE, which ensembles multiple pre-trained models to improve separation of novel and old classes. TLCE minimizes interference between old and new classes by mapping old class images to quasi-orthogonal prototypes using episodic training. It then ensembles diverse pre-trained models to better adapt to novel classes despite data imbalance. Extensive experiments on various datasets demonstrate that our transfer learning ensemble approach outperforms state-of-the-art FSCIL methods.

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BibTeX RIS

Shuangmei Wang, Yang Cao, Tieru Wu. 2023-12-07. TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning. https://arxiv.org/abs/2312.04225

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