Association Restoration Test: Revealing Restorable Shortcuts after Unlearning
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Computer Science > Computer Vision and Pattern Recognition
Title:Association Restoration Test: Revealing Restorable Shortcuts after Unlearning
Abstract:Association unlearning aims to disable learned label-attribute shortcuts while preserving task performance. Existing evaluations mainly measure output-level robustness or probe whether shortcut attributes remain readable in frozen features, but neither test determines whether a retained association remains functionally usable by the original classifier. We propose the Association Restoration Test (ART), a post-hoc diagnostic for functional shortcut restorability. ART estimates class-conditional association directions, amplifies residual components, and evaluates the modified features with the original classifier head. Across Waterbirds, CelebA, SpuCoDogs, and an ISIC timestamp-artifact extension, we show that output metrics, representation probes, and ART characterize distinct aspects of shortcut mitigation. These findings motivate restoration-aware evaluation for unlearning and shortcut-mitigation methods that target learned associations rather than individual classes or concepts.
| Comments: | Preprint. 16 pages |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.05726 [cs.CV] |
| (or arXiv:2607.05726v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05726
arXiv-issued DOI via DataCite (pending registration)
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