不要破坏边界：基于自由能排斥的持续性无学习用于OOD检测
Don't Break the Boundary: Continual Unlearning for OOD Detection Based on Free Energy Repulsion
AI总结：
本文提出TFER框架，通过自由能原理构建Push-Pull机制，实现边界保持的持续无学习，提升OOD检测性能和模型稳定性。
摘要：
在开放世界环境中部署可信AI面临双重挑战：一方面需要鲁棒的分布外（OOD）检测以确保系统安全，另一方面需要灵活的机器无学习以满足隐私合规和模型修正的需求。然而，这一目标遇到了根本性的几何矛盾：当前的OOD检测器依赖于静态且紧凑的数据流形，而传统的以分类为导向的无学习方法会破坏这种精细的结构，导致模型在辨别异常方面的能力丧失，同时擦除目标类别。为了解决这一困境，我们首先定义了边界保持类别无学习的问题，并提出了一种关键的概念转变：在OOD检测的背景下，有效的无学习在数学上等同于将目标类别转换为OOD样本。基于此，我们提出了TFER（总自由能排斥）框架。受自由能原理的启发，TFER构建了一个新的Push-Pull游戏机制：通过拉机制将保留的类别锚定在低能量ID流形上，同时通过自由能排斥力主动将遗忘的类别推送到高能量OOD区域。该方法通过参数高效的微调实现，避免了全重训练的高昂成本。广泛的实验表明，TFER在精确无学习的同时，最大限度地保持了模型在剩余类别和外部OOD数据上的辨别性能。更重要的是，我们的研究揭示了TFER独特的Push-Pull平衡使模型具有内在的结构稳定性，使其能够有效抵抗灾难性遗忘，而无需复杂的额外约束，从而在持续无学习任务中展现出卓越的潜力。
英文摘要：
Deploying trustworthy AI in open-world environments faces a dual challenge: the necessity for robust Out-of-Distribution (OOD) detection to ensure system safety, and the demand for flexible machine unlearning to satisfy privacy compliance and model rectification. However, this objective encounters a fundamental geometric contradiction: current OOD detectors rely on a static and compact data manifold, whereas traditional classification-oriented unlearning methods disrupt this delicate structure, leading to a catastrophic loss of the model's capability to discriminate anomalies while erasing target classes. To resolve this dilemma, we first define the problem of boundary-preserving class unlearning and propose a pivotal conceptual shift: in the context of OOD detection, effective unlearning is mathematically equivalent to transforming the target class into OOD samples. Based on this, we propose the TFER (Total Free Energy Repulsion) framework. Inspired by the free energy principle, TFER constructs a novel Push-Pull game mechanism: it anchors retained classes within a low-energy ID manifold through a pull mechanism, while actively expelling forgotten classes to high-energy OOD regions using a free energy repulsion force. This approach is implemented via parameter-efficient fine-tuning, circumventing the prohibitive cost of full retraining. Extensive experiments demonstrate that TFER achieves precise unlearning while maximally preserving the model's discriminative performance on remaining classes and external OOD data. More importantly, our study reveals that the unique Push-Pull equilibrium of TFER endows the model with inherent structural stability, allowing it to effectively resist catastrophic forgetting without complex additional constraints, thereby demonstrating exceptional potential in continual unlearning tasks.
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。
