Analytical Contrastive Projection for Accurate Continual Learning
Saleh Momeni, Changnan Xiao, Bing Liu
This paper studies the class-incremental learning (CIL) setting of continual learning. CIL aims to learn a sequence of tasks, where each task consists of a set of classes. Traditional CIL methods does not use a pre-trained model (PTM) and suffer from catastrophic forgetting (CF) due to their need to incrementally learn both feature representations and the classifier. The incorporation of PTMs into CIL has led to the development of computationally efficient methods that treat the PTM as a feature extractor paired with analytical classifiers. These methods often achieve state-of-the-art performance in CIL. However, they still face a major limitation: the inability to continually adapt or update feature representations incrementally to best suit the specific CIL tasks, leading to suboptimal performance. To overcome this, we propose ACP (Analytical Contrastive Projection), a novel method that retains the computational efficiency and stability of analytical classifiers while enabling incremental feature adaptation without gradient-based training. Our experiments demonstrate that ACP not only outperforms strong baselines but also matches the accuracy of joint training, which is regarded as the upper bound of CIL.