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.

翻译：少样本类增量学习（FSCIL）面临在极少样本条件下逐步识别新类别的挑战，同时需避免对旧类别的灾难性遗忘或对新类别的过拟合。我们提出的TLCE方法集成了多个预训练模型，以改善新旧类别的区分能力。TLCE通过情景训练将旧类别图像映射至准正交原型，从而最小化新旧类别间的干扰；并集成多样化的预训练模型以在数据不平衡条件下更好地适应新类别。在多个数据集上的广泛实验表明，我们的迁移学习集成方法优于当前最先进的FSCIL方法。