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

• URL: http://arxiv.org/abs/2312.04225v1
• Date: Thu, 7 Dec 2023 11:16:00 GMT
• Title: TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning
• Authors: Shuangmei Wang, Yang Cao, Tieru Wu
• Abstract summary: Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples. We propose TLCE, which ensembles multiple pre-trained models to improve separation of novel and old classes.
• Score: 5.753740302289126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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