Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness

• URL: http://arxiv.org/abs/2509.11355v1
• Date: Sun, 14 Sep 2025 17:14:07 GMT
• Title: Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness
• Authors: Robin Narsingh Ranabhat, Longwei Wang, Amit Kumar Patel, KC santosh,
• Abstract summary: CNNs excel at image classification but remain vulnerable to common corruptions that humans handle with ease.<n>We propose two complementary regularization strategies designed to encourage shape-biased representations.<n>Our results suggest that loss-level regularization can effectively steer CNNs toward more shape-aware, resilient representations.
• Score: 2.558238597112103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is their reliance on local texture cues rather than global object shapes -- a stark contrast to human perception. To address this, we propose two complementary regularization strategies designed to encourage shape-biased representations and enhance robustness. The first introduces an auxiliary loss that enforces feature consistency between original and low-frequency filtered inputs, discouraging dependence on high-frequency textures. The second incorporates supervised contrastive learning to structure the feature space around class-consistent, shape-relevant representations. Evaluated on the CIFAR-10-C benchmark, both methods improve corruption robustness without degrading clean accuracy. Our results suggest that loss-level regularization can effectively steer CNNs toward more shape-aware, resilient representations.

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