When Prompt-based Incremental Learning Does Not Meet Strong Pretraining

TL;DR

This paper introduces an Adaptive Prompt Generator (APG) that unifies prompt retrieval and learning, reducing reliance on strong pretraining and improving incremental learning performance without catastrophic forgetting.

Contribution

We propose a learnable APG that integrates prompt retrieval and learning, regularized by a knowledge pool, to enhance incremental learning without strong pretraining.

Findings

Outperforms existing exemplar-free incremental learning methods.

Maintains competitive performance under strong retraining.

Effectively reduces the gap impact between pretraining and new tasks.

Abstract

Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based methods adopt a fixed backbone to sequential tasks by learning task-specific prompts. However, existing prompt-based methods heavily rely on strong pretraining (typically trained on ImageNet-21k), and we find that their models could be trapped if the potential gap between the pretraining task and unknown future tasks is large. In this work, we develop a learnable Adaptive Prompt Generator (APG). The key is to unify the prompt retrieval and prompt learning processes into a learnable prompt generator. Hence, the whole prompting process can be optimized to reduce the negative effects of the gap between tasks effectively. To make our APG avoid learning ineffective knowledge, we maintain a knowledge pool to regularize APG with the feature distribution of each class. Extensive experiments show that our method significantly outperforms advanced methods in exemplar-free incremental learning without (strong) pretraining. Besides, under strong retraining, our method also has comparable performance to existing prompt-based models, showing that our method can still benefit from pretraining. Codes can be found at https://github.com/TOM-tym/APG