CAM-Based Methods Can See Through Walls - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

CAM-Based Methods Can See Through Walls

Résumé

CAM-based methods are widely-used post-hoc interpretability method that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior.
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Dates et versions

hal-04683092 , version 1 (01-09-2024)

Identifiants

Citer

Magamed Taimeskhanov, Ronan Sicre, Damien Garreau. CAM-Based Methods Can See Through Walls. ECML PKDD 2024, Sep 2024, Vilnius (Lituanie), Lithuania. pp.332-348, ⟨10.1007/978-3-031-70368-3_20⟩. ⟨hal-04683092⟩
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