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arXiv AI By Khawaja Murad ul Hassan, Mehran Ebrahimi

Signal or Noise? Auditing Rotation-Induced Saliency Drift in Medical and Aerial Imaging

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The paper investigates why post‑hoc saliency maps, such as Grad‑CAM, shift when input images are rotated, even if the model’s prediction remains unchanged. By measuring equivariance at each stage of the CAM operator, the authors find that the instability originates from the spatial activation tensor rather than the channel weights, and that a training‑free wrapper called EquiGrad‑CAM can align and average saliency maps across rotated views to significantly improve rotation equivariance. Experiments on ImageNet, PatchCamelyon, and RESISC45 demonstrate that EquiGrad‑CAM outperforms rotation‑augmented training and enhances zero‑shot CLIP explanations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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