基于标签条件能量幅度正则化的机器遗忘中的跨域泛化
Cross-Domain Generalization in Machine Unlearning via Label-Conditioned Energy Magnitude Regularization
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中文总结 AI 辅助
本文提出基于标签条件能量幅度正则化的机器遗忘方法，结合DINOv2相似度传播，在DomainNet和CIFAR-10上实现跨域高效遗忘且不损害其余类别性能。
中文摘要 AI 辅助
机器遗忘用于移除训练模型中特定数据的影响，但多数方法将被遗忘的概念视为孤立的。本文研究当某个类别被遗忘时模型其余部分的变化，采用基于标签的能量模型（EBM）为每个类别分配能量，使遗忘效果可直接观测。我们通过提升某类别图像-标签对的能量来实现该类别的遗忘，训练过程包含遗忘项、预训练模型的保留锚点、全局间隔及能量正则化项（防止能量幅度无限制增长）。传播项将相同的遗忘信号应用于保留样本，权重为每个样本与被遗忘类别的DINOv2相似度，确保遗忘作用于与被遗忘类别相似的图像，同时不影响其余样本。我们在两个基准数据集上进行评估：1）在DomainNet的四个视觉域子集上，依次遗忘老虎、狮子和剪刀类别，在草图域中遗忘某类别会同步擦除其在真实、剪贴画及绘画域中的存在，狮子和剪刀的遗忘误差分别达到98%和99%，且效果会传递至最相似的类别；2）在CIFAR-10数据集上，关闭传播项后单独遗忘十个类别中的每一个，遗忘效果完全（100%），其余九个类别平均保留遗忘前98.5%的准确率。
英文摘要
Machine unlearning removes the influence of specific data from a trained model. However, most methods treat the forgotten concept as isolated. In this paper, we study what happens to the rest of the model when a class is forgotten, using a label-conditioned energy-based model (EBM) that assigns per-class energies, making the effect directly observable. We forget a class by raising the energy of its image-label pairs, training with a forget term, a retain anchor to the pretrained model, a global margin, and an energy regularizer that stops the energy magnitudes from growing without limit. A propagation term applies the same forget signal to retain samples, weighted by each sample's DINOv2 similarity to the forget class, so forgetting reaches images that resemble it and leaves the rest untouched. We evaluate on two benchmark datasets: 1) On a subset of DomainNet across four visual domains, we forget tiger, lion, and scissors one at a time. Forgetting a class in the sketch domain also erases it from real, clipart, and painting, with forgetting error reaching 98% and 99% for lion and scissors, and the effect carrying over to the most similar class. 2) On CIFAR-10, we turn off the propagation term and forget each of the ten classes on its own. Forgetting is complete (100%), while the other nine classes retain 98.5% of their pre-unlearning accuracy on average.
发表机构
- National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）
- Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。
