Continual learning (CL) aims to extend deep models from static and enclosed
environments to dynamic and complex scenarios, enabling systems to continuously
acquire new knowledge of novel categories without forgetting previously learned
knowledge. Recent CL models have gradually shifted towards the utilization of
pre-trained models (PTMs) with parameter-efficient fine-tuning (PEFT)
strategies. However, continual fine-tuning still presents a serious challenge
of catastrophic forgetting due to the absence of previous task data.
Additionally, the fine-tune-then-frozen mechanism suffers from performance
limitations due to feature channels suppression and insufficient training data
in the first CL task. To this end, this paper proposes feature transformation
tuning (FeTT) model to non-parametrically fine-tune backbone features across
all tasks, which not only operates independently of CL training data but also
smooths feature channels to prevent excessive suppression. Then, the extended
ensemble strategy incorporating different PTMs with FeTT model facilitates
further performance improvement. We further elaborate on the discussions of the
fine-tune-then-frozen paradigm and the FeTT model from the perspectives of
discrepancy in class marginal distributions and feature channels. Extensive
experiments on CL benchmarks validate the effectiveness of our proposed method.

本文提出了一种名为特征转换调优（FeTT）模型，它通过非参数化地对骨干特征进行微调，实现了对所有任务的独立操作，并且能够平滑特征通道以避免过度抑制，结合不同的预训练模型进一步改善性能，实验证明了我们提出方法的有效性。