Counterfactual Explanations on Robust Perceptual Geodesics

• URL: http://arxiv.org/abs/2601.18678v1
• Date: Mon, 26 Jan 2026 16:52:54 GMT
• Title: Counterfactual Explanations on Robust Perceptual Geodesics
• Authors: Eslam Zaher, Maciej Trzaskowski, Quan Nguyen, Fred Roosta,
• Abstract summary: We introduce Perceptual Counterfactual Geodesics (PCG), a method that constructs counterfactuals by tracing geodesics under a metric induced from robust vision features.<n>This geometry aligns with human perception and penalizes brittle directions, enabling smooth, on-manifold, semantically valid transitions.<n>Experiments on three vision datasets show that PCG outperforms baselines and reveals failure modes hidden under standard metrics.
• Score: 13.054357482525505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latent-space optimization methods for counterfactual explanations - framed as minimal semantic perturbations that change model predictions - inherit the ambiguity of Wachter et al.'s objective: the choice of distance metric dictates whether perturbations are meaningful or adversarial. Existing approaches adopt flat or misaligned geometries, leading to off-manifold artifacts, semantic drift, or adversarial collapse. We introduce Perceptual Counterfactual Geodesics (PCG), a method that constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric induced from robust vision features. This geometry aligns with human perception and penalizes brittle directions, enabling smooth, on-manifold, semantically valid transitions. Experiments on three vision datasets show that PCG outperforms baselines and reveals failure modes hidden under standard metrics.

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