# Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets Source: [Robustmix: Improving Robustness by Regularizing the Frequency Bias of Deep Nets]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:31:06.502Z Authors: Jonas Ngnawe, Marianne Abemgnigni Njifon, Jonathan Heek, Yann Dauphin Published: 2023-04-06T03:24:00.000Z ## Abstract Deep networks have achieved impressive results on a range of well-curated benchmark datasets. Surprisingly, their performance remains sensitive to perturbations that have little effect on human performance. In this work, we propose a novel extension of Mixup called Robustmix that regularizes networks to classify based on lower-frequency spatial features. We show that this type of regularization improves robustness on a range of benchmarks such as Imagenet-C and Stylized Imagenet. It adds little computational overhead and, furthermore, does not require a priori knowledge of a large set of image transformations. We find that this approach further complements recent advances in model architecture and data augmentation, attaining a state-of-the-art mCE of 44.8 with an EfficientNet-B8 model and RandAugment, which is a reduction of 16 mCE compared to the baseline. ## 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]()