Models predicting human authenticity judgments produce inconsistent attribution maps across architectures, showing that explanations are non-identifiable.
Imagenet-trained cnns are not bi- ased towards texture: Revisiting feature reliance through controlled suppression.arXiv preprint arXiv:2509.20234
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Non-identifiability of Explanations from Model Behavior in Deep Networks of Image Authenticity Judgments
Models predicting human authenticity judgments produce inconsistent attribution maps across architectures, showing that explanations are non-identifiable.