Re-Evaluating Continual Learning with Few-Shot Adaptation

2026-06-02

AI Summary

This work addresses the limitations of conventional continual learning evaluation, which relies on zero-shot forgetting metrics and fails to comprehensively capture a model’s ability to balance retention of prior knowledge with adaptation to new tasks. To overcome this, the authors propose a few-shot continual learning evaluation framework that introduces a novel metric—“per-sample plasticity”—and integrates meta-learning to prospectively model future tasks, thereby enhancing the model’s ability to learn how to learn. Through fine-grained analysis of task sequences in continual image classification, the study systematically reveals the behavioral characteristics of mainstream continual learning algorithms under few-shot settings and demonstrates that the proposed prospective mechanism significantly improves cross-task adaptability.

Abstract

Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequence of tasks. The standard measure of stability (i.e., forgetting) is the 0-shot performance of a model on previously learned tasks, and plasticity, the performance on the most recently learned task. However, 0-shot evaluation does not fully measure a model or method's ability to retain learned information or adapt quickly to new information, as it requires perfect recall across multiple tasks. In this paper, we propose few-shot evaluation as a more comprehensive assessment of the stability and plasticity of a continual learning system. We conduct a fine-grained assessment on task sequences for continual image classification and find that this paradigm produces novel insights into the performance of popular continual learning strategies. Through few-shot evaluation with a novel metric -- per-shot plasticity -- we show that adding `foresight' to continual learning methods via the meta-learning of a short sequence of future tasks induces learning-to-learn behavior over the task sequence.

Problem

• continual learning
• few-shot adaptation
• stability
• plasticity
• evaluation metric

Innovation

• few-shot evaluation
• continual learning
• per-shot plasticity
• meta-learning
• learning-to-learn