PrePrompt: Predictive prompting for class incremental learning

TL;DR

PrePrompt introduces a novel class incremental learning framework that predicts task-specific prompts using pre-trained models, effectively balancing stability and plasticity to outperform existing prompt-based methods.

Contribution

The paper proposes PrePrompt, a new CIL framework that predicts prompts directly from pre-trained models, overcoming correlation-based limitations and improving performance.

Findings

PrePrompt outperforms state-of-the-art prompt-based CIL methods on multiple benchmarks.

Feature translation helps balance stability and plasticity in incremental learning.

The approach effectively mitigates bias towards recent classes.

Abstract

Class Incremental Learning (CIL) based on pre-trained models offers a promising direction for open-world continual learning. Existing methods typically rely on correlation-based strategies, where an image's classification feature is used as a query to retrieve the most related key prompts and select the corresponding value prompts for training. However, these approaches face an inherent limitation: fitting the entire feature space of all tasks with only a few trainable prompts is fundamentally challenging. We propose Predictive Prompting (PrePrompt), a novel CIL framework that circumvents correlation-based limitations by leveraging pre-trained models' natural classification ability to predict task-specific prompts. Specifically, PrePrompt decomposes CIL into a two-stage prediction framework: task-specific prompt prediction followed by label prediction. While theoretically appealing, this framework risks bias toward recent classes due to missing historical data for older classifier calibration. PrePrompt then mitigates this by incorporating feature translation, dynamically balancing stability and plasticity. Experiments across multiple benchmarks demonstrate PrePrompt's superiority over state-of-the-art prompt-based CIL methods. Code available at \href{github.com/libo-huang/preprompt}{github.com/libo-huang/preprompt}.