  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.
