表示遗忘：通过信息压缩实现遗忘
Representation Unlearning: Forgetting through Information Compression
AI总结：
提出表示遗忘框架，通过在模型表示空间学习信息瓶颈变换来直接执行遗忘，无需修改模型参数，实现可靠遗忘、保持效用和计算高效。
摘要：
机器遗忘旨在消除特定训练数据对模型的影响，这一需求由隐私法规和鲁棒性关注驱动。现有方法通常修改模型参数，但此类更新可能不稳定、计算成本高且受局部近似限制。我们引入表示遗忘，一个直接在模型表示空间中执行遗忘的框架。我们不修改模型参数，而是学习一个对表示施加信息瓶颈的变换：最大化与保留数据的互信息，同时抑制关于待遗忘数据的信息。我们推导出使这一目标可处理的变分替代，并展示如何在两种实际场景中实例化：当保留和遗忘数据都可用时，以及在仅能访问遗忘数据的零样本设置中。在多个基准上的实验表明，与以参数为中心的基线相比，表示遗忘实现了更可靠的遗忘、更好的效用保持和更高的计算效率。
英文摘要：
Machine unlearning seeks to remove the influence of specific training data from a model, a need driven by privacy regulations and robustness concerns. Existing approaches typically modify model parameters, but such updates can be unstable, computationally costly, and limited by local approximations. We introduce Representation Unlearning, a framework that performs unlearning directly in the model's representation space. Instead of modifying model parameters, we learn a transformation over representations that imposes an information bottleneck: maximizing mutual information with retained data while suppressing information about data to be forgotten. We derive variational surrogates that make this objective tractable and show how they can be instantiated in two practical regimes: when both retain and forget data are available, and in a zero-shot setting where only forget data can be accessed. Experiments across several benchmarks demonstrate that Representation Unlearning achieves more reliable forgetting, better utility retention, and greater computational efficiency than parameter-centric baselines.
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。
