ERASE：无需显式机器遗忘的即时遗忘
On-the-go Forgetting without Explicit Unlearning via ERASE
- Tata Consultancy Services Research, Mumbai（塔塔咨询服务研究院，孟买）
- Indian Institute of Technology Bombay（印度理工学院孟买分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ERASE通过推理时类别条件输入扰动实现无需权重修改的即时遗忘，在保持其他子类预测的同时可证明地遗忘指定子类，兼顾效果、效率与保真度。
AI中文摘要：
现有的机器遗忘方法通常依赖于事后权重调整或知识蒸馏，导致内存成本重复、泛化性能下降且可扩展性有限。在本工作中，我们提出了ERASE（通过重构对抗信号编辑实现擦除），一种用于即时遗忘的框架，它通过在不修改模型权重的情况下抑制私有数据的可观察影响。ERASE利用结构化的、类别条件化的输入扰动，在推理过程中诱导选择性遗忘，从而无需重新训练、微调或模型副本。我们严格刻画了ERASE在温和正则性假设下，能够可证明地实现对指定子类的功能性遗忘，同时保持同一超类内其他子类预测的充分条件。这一分析为推理时遗忘提供了原则性的理论基础。在多种架构和基准数据集上，与近期基于机器遗忘的方法相比，ERASE在遗忘效果、计算效率和保留保真度之间保持了最佳观测平衡。通过将数据移除重新构想为无需机器遗忘的遗忘，我们的工作为持续、隐私意识的学习建立了一条可扩展、符合法规的路径。
英文摘要：
Existing unlearning approaches typically rely on post hoc weight adaptation or distillation, leading to duplicated memory costs, degraded generalization, and limited scalability. In this work, we introduce ERASE, Erasure via Reconstructive Adversarial Signal Editing, a framework for on-the-go forgetting that suppresses the observable influence of private data without modifying model weights. ERASE leverages structured, class-conditioned input perturbations to induce selective forgetting during inference, eliminating the need for retraining, fine-tuning, or model copies. We rigorously characterize sufficient conditions when ERASE provably achieves functional forgetting of designated subclasses while preserving predictions across other subclasses within the same superclass. This analysis offers a principled foundation for inference-time forgetting under mild regularity assumptions. Across diverse architectures and benchmark datasets, ERASE maintains the best observed balance between forgetting efficacy, computational efficiency, and retention fidelity over recent unlearning-based methods. By reimagining data removal as forgetting without unlearning, our work establishes a scalable, regulation-aligned pathway for continual, privacy-conscious learning.
