用语言引导的概念瓶颈模型，让持续学习既记牢知识又看得懂决策。
Language Guided Concept Bottleneck Models for Interpretable Continual Learning
- 用概念瓶颈层结合CLIP语义对齐，学可解释的跨任务概念
- 在ImageNet子集上最终平均准确率提升最多3.06%
- 提供概念可视化，适合关注可解释性的持续学习研究者
持续学习（CL）旨在使学习系统能不断获取新知识而不遗忘旧知识。现有方法多聚焦于缓解灾难性遗忘以提升性能，但随着新信息引入，理解模型决策过程的可解释性愈发重要，却少被关注。本文提出一种融合语言引导概念瓶颈模型（CBMs）的新框架，利用概念瓶颈层与CLIP模型对齐语义，学习可跨任务泛化的、人类可理解的概念。通过聚焦可解释概念，该方法不仅增强了知识长期保留能力，还提供了透明的决策洞察。实验表明，在多个数据集上表现优越，尤其在ImageNet-subset上最终平均准确率提升达3.06%。此外，我们还展示了模型预测的概念可视化，进一步推动了可解释持续学习的发展。
原文摘要 · Abstract (English)
Continual learning (CL) aims to enable learning systems to acquire new knowledge constantly without forgetting previously learned information. CL faces the challenge of mitigating catastrophic forgetting while maintaining interpretability across tasks. Most existing CL methods focus primarily on preserving learned knowledge to improve model performance. However, as new information is introduced, the interpretability of the learning process becomes crucial for understanding the evolving decision-making process, yet it is rarely explored. In this paper, we introduce a novel framework that integrates language-guided Concept Bottleneck Models (CBMs) to address both challenges. Our approach leverages the Concept Bottleneck Layer, aligning semantic consistency with CLIP models to learn human-understandable concepts that can generalize across tasks. By focusing on interpretable concepts, our method not only enhances the models ability to retain knowledge over time but also provides transparent decision-making insights. We demonstrate the effectiveness of our approach by achieving superior performance on several datasets, outperforming state-of-the-art methods with an improvement of up to 3.06% in final average accuracy on ImageNet-subset. Additionally, we offer concept visualizations for model predictions, further advancing the understanding of interpretable continual learning.
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