← 研踪 PaperTrailCVF · CVPR 2026:cvf:aa384901884df059170087e6fbb604d6 · 记录 #1
Designing to Forget: Deep Semi-parametric Models for Unlearning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:39
摘要
Recent advances in machine unlearning have focused on developing algorithms to remove specific training samples from a trained model. In contrast, we observe that not all models are equally easy to unlearn. Hence, we introduce a family of deep semi-parametric models (SPMs) that exhibit non-parametric behavior during unlearning. SPMs use a fusion module that aggregates information from each training sample, enabling explicit test-time deletion of selected samples without altering model parameters. Empirically, we demonstrate that SPMs achieve competitive task performance to parametric models in image classification and generation, while being significantly more efficient for unlearning. Notably, on ImageNet classification, SPMs reduce the prediction gap relative to a retrained (oracle) baseline by 11% and achieve over 10xfaster unlearning compared to existing approaches on parametric models.
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记录 #1 · 2026 · 最新观测版本
Designing to Forget: Deep Semi-parametric Models for Unlearning
Recent advances in machine unlearning have focused on developing algorithms to remove specific training samples from a trained model. In contrast, we observe that not all models are equally easy to unlearn. Hence, we introduce a family of deep semi-parametric models (SPMs) that exhibit non-parametric behavior during unlearning. SPMs use a fusion module that aggregates information from each training sample, enabling explicit test-time deletion of selected samples without altering model parameters. Empirically, we demonstrate that SPMs achieve competitive task performance to parametric models in image classification and generation, while being significantly more efficient for unlearning. Notably, on ImageNet classification, SPMs reduce the prediction gap relative to a retrained (oracle) baseline by 11% and achieve over 10xfaster unlearning compared to existing approaches on parametric models.
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