一种用于评估灾难性遗忘的共形预测度量方法
A Conformal Predictive Measure for Assessing Catastrophic Forgetting
AI总结：
本研究提出基于共形预测的新指标CPCF，通过监测模型对旧任务的置信度量化持续学习中的灾难性遗忘，经四个基准数据集验证其与旧任务准确率强相关，可作为动态学习环境下评估灾难性遗忘的可靠工具。
摘要：
本研究提出了一种评估持续学习中灾难性遗忘（CF）的新方法。我们提出了一种基于共形预测（CP）的新指标，称为共形预测置信因子（CPCF），用于有效量化和评估灾难性遗忘。该框架利用自适应共形预测，通过监测模型对先前已学习任务的置信度来估计遗忘程度。该方法为监测和衡量新任务引入时旧任务的灾难性遗忘提供了动态且实用的解决方案，更适用于现实世界应用。在四个基准数据集上的实验结果表明，CPCF与旧任务准确率之间存在强相关性，验证了所提指标的可靠性和可解释性。研究结果凸显了CPCF作为评估和理解动态学习环境中灾难性遗忘的可靠有效工具的潜力。
英文摘要：
This work introduces a novel methodology for assessing catastrophic forgetting (CF) in continual learning. We propose a new conformal prediction (CP)-based metric, termed the Conformal Prediction Confidence Factor (CPCF), to quantify and evaluate CF effectively. Our framework leverages adaptive CP to estimate forgetting by monitoring the model's confidence on previously learned tasks. This approach provides a dynamic and practical solution for monitoring and measuring CF of previous tasks as new ones are introduced, offering greater suitability for real-world applications. Experimental results on four benchmark datasets demonstrate a strong correlation between CPCF and the accuracy of previous tasks, validating the reliability and interpretability of the proposed metric. Our results highlight the potential of CPCF as a robust and effective tool for assessing and understanding CF in dynamic learning environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
