# Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness Source: [Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:38:31.803Z Authors: Robin Narsingh Ranabhat, Longwei Wang, Amit Kumar Patel, KC santosh Published: 2025-09-14T17:14:07.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()