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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.18678 |
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| _version_ | 1866912934019465216 |
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| author | Zaher, Eslam Trzaskowski, Maciej Nguyen, Quan Roosta, Fred |
| author_facet | Zaher, Eslam Trzaskowski, Maciej Nguyen, Quan Roosta, Fred |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18678 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Counterfactual Explanations on Robust Perceptual Geodesics Zaher, Eslam Trzaskowski, Maciej Nguyen, Quan Roosta, Fred Machine Learning Computer Vision and Pattern Recognition Human-Computer Interaction Differential Geometry 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. |
| title | Counterfactual Explanations on Robust Perceptual Geodesics |
| topic | Machine Learning Computer Vision and Pattern Recognition Human-Computer Interaction Differential Geometry |
| url | https://arxiv.org/abs/2601.18678 |