ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression

arXiv — cs.CVWednesday, October 29, 2025 at 4:00:00 AM
Recent research challenges the long-held belief that Convolutional Neural Networks (CNNs) are biased towards texture in their feature reliance. By revisiting the cue-conflict experiment conducted by Geirhos et al., the study introduces a new framework that systematically suppresses various cues like shape, texture, and color. This approach aims to provide a clearer understanding of how CNNs utilize different features, which is crucial for advancing deep learning methodologies and improving model performance.
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