Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?
Post-hoc feature attribution methods are widely deployed in safety-critical vision systems, yet their stability under realistic input perturbations remains poorly characterized. Existing metrics evaluate explanations primarily under additive noise, collapse stability to a single scalar, and fail to condition on prediction preservation, conflating explanation fragility with model sensitivity. We introduce the Feature Attribution Stability Suite (FASS), a benchmark that enforces prediction-invariance filtering, decomposes stability into three complementary metrics: structural similarity, rank correlation, and top-k Jaccard overlap-and evaluates across geometric, photometric, and compression...
| Authors | Kamalasankari Subramaniakuppusamy, Jugal Gajjar |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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