aiqbench analysis
This paper challenges the standard 0-shot stability metric in continual learning by introducing few-shot evaluation to better capture a model's ability to retain information and adapt quickly. The authors demonstrate that incorporating "foresight" through meta-learning of future tasks induces effective learning-to-learn behaviors, offering novel insights into the performance of existing strategies. This shift in evaluation methodology provides a more realistic assessment of how models handle sequential task sequences without requiring perfect recall.
Re-Evaluating Continual Learning with Few-Shot Adaptation
arXiv:2606.03843v1 Announce Type: cross 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.
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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.03843 [cs.LG]
(or arXiv:2606.03843v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.03843 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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