通过不确定性量化应对虚假遗忘
Tackling Fake Forgetting through Uncertainty Quantification
AI总结：
本文提出CR指标和CPU框架，通过置信预测提升遗忘质量，解决传统遗忘评估的可靠性问题。
摘要：
通过不确定性量化应对虚假遗忘
英文摘要：
Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric for assessing unlearning performance, it falls short in assessing the reliability of forgetting. In this paper, we find that the forgetting data points misclassified by unlearning accuracy still have their ground truth labels included in the conformal prediction set from the uncertainty quantification perspective, leading to a phenomenon we term fake forgetting. To address this issue, we propose a novel metric CR, inspired by conformal prediction, that offers a more reliable assessment of forgetting quality. Building on these insights, we further propose an unlearning framework CPU that incorporates conformal prediction into the Carlini & Wagner adversarial attack loss, enabling the ground truth label to be effectively removed from the conformal prediction set. Through extensive experiments on image classification tasks, we demonstrate both the effectiveness of our proposed metric and the superior forgetting quality achieved by our framework. Code is available at https://github.com/TIML-Group/Conformal-Prediction-Unlearning.
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。
