# Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification

arXiv: https://arxiv.org/abs/2403.01944 · HTML: https://mlsanity.com/paper/2403.01944/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 51 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 30 of 37)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [A Fourier Perspective on Model Robustness in Computer Vision](https://mlsanity.com/paper/1906.08988/) (2019)
- [Escaping the Big Data Paradigm with Compact Transformers](https://mlsanity.com/paper/2104.05704/) (2021)
- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://mlsanity.com/paper/2103.10158/) (2021)
- [Improving the Robustness of Deep Neural Networks via Stability Training](https://mlsanity.com/paper/1604.04326/) (2016)
- [Frequency Domain Model Augmentation for Adversarial Attack](https://mlsanity.com/paper/2207.05382/) (2022)
- [Benchmarking the Robustness of Semantic Segmentation Models](https://mlsanity.com/paper/1908.05005/) (2019)
- [On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness](https://mlsanity.com/paper/2102.11273/) (2021)
- [AugMax: Adversarial Composition of Random Augmentations for Robust Training](https://mlsanity.com/paper/2110.13771/) (2021)
- [Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain](https://mlsanity.com/paper/2108.08487/) (2021)
- [MixGen: A New Multi-Modal Data Augmentation](https://mlsanity.com/paper/2206.08358/) (2022)
- [Learning Loss for Test-Time Augmentation](https://mlsanity.com/paper/2010.11422/) (2020)
- [PRIME: A few primitives can boost robustness to common corruptions](https://mlsanity.com/paper/2112.13547/) (2021)
- [TeachAugment: Data Augmentation Optimization Using Teacher Knowledge](https://mlsanity.com/paper/2202.12513/) (2022)
- [Improving robustness against common corruptions with frequency biased models](https://mlsanity.com/paper/2103.16241/) (2021)
- [What do neural networks learn in image classification? A frequency shortcut perspective](https://mlsanity.com/paper/2307.09829/) (2023)
- [A Survey on the Robustness of Computer Vision Models against Common Corruptions](https://mlsanity.com/paper/2305.06024/) (2023)
- [Automatic Data Augmentation via Invariance-Constrained Learning](https://mlsanity.com/paper/2209.15031/) (2022)
- [Improving Generalization in Visual Reinforcement Learning via Conflict-aware Gradient Agreement Augmentation](https://mlsanity.com/paper/2308.01194/) (2023)
- [HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustness](https://mlsanity.com/paper/2307.11823/) (2023)
- [Soft Augmentation for Image Classification](https://mlsanity.com/paper/2211.04625/) (2022)

## Built on by (most cited first, 18 of 18)

- [PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection](https://mlsanity.com/paper/2412.11807/) (2024)
- [PushPull-Net: Inhibition-driven ResNet robust to image corruptions](https://mlsanity.com/paper/2408.04077/) (2024)
- [Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation](https://mlsanity.com/paper/2505.10223/) (2025)
- [Pseudo Multi-Source Domain Generalization: Bridging the Gap Between Single and Multi-Source Domain Generalization](https://mlsanity.com/paper/2505.23173/) (2025)
- [Target-Agnostic Calibration under Distribution Shift with Frequency-Aware Gradient Rectification](https://mlsanity.com/paper/2508.19830/) (2025)
- [Deep Edge Filter: Return of the Human-Crafted Layer in Deep Learning](https://mlsanity.com/paper/2510.13865/) (2025)
- [Improving Variational Autoencoder using Random Fourier Transformation: An Aviation Safety Anomaly Detection Case-Study](https://mlsanity.com/paper/2601.01016/) (2026)
- [Unsupervised Modular Adaptive Region Growing and RegionMix Classification for Wind Turbine Segmentation](https://mlsanity.com/paper/2601.04065/) (2026)
- [Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection](https://mlsanity.com/paper/2608.01348/) (2026)
- [Un-mixing Test-time Adaptation under Heterogeneous Data Streams](https://mlsanity.com/paper/2411.15173/) (2024)
- [BGM: Background Mixup for X-ray Prohibited Items Detection](https://mlsanity.com/paper/2412.00460/) (2024)
- [Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction](https://mlsanity.com/paper/2504.19086/) (2025)
- [DART: A Server-side Plug-in for Resource-efficient Robust Federated Learning](https://mlsanity.com/paper/2508.17381/) (2025)
- [Rethinking Bias in Generative Data Augmentation for Medical AI: a Frequency Recalibration Method](https://mlsanity.com/paper/2511.12301/) (2025)
- [FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI](https://mlsanity.com/paper/2511.12899/) (2025)
- [MoireMix: A Formula-Based Data Augmentation for Improving Image Classification Robustness](https://mlsanity.com/paper/2603.25109/) (2026)
- [Robustness Emerges Early in Training Dynamics, but Is Not Preserved](https://mlsanity.com/paper/2608.04442/) (2026)
- [Suppress and Diversify: Refining Robust Pathways for Corruption Robustness](https://mlsanity.com/paper/2608.06712/) (2026)

## Abstract (arXiv)

Computer vision models normally witness degraded performance when deployed in real-world scenarios, due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue, as it aims to increase data variety and reduce the distribution gap between training and test data. However, common visual augmentations might not guarantee extensive robustness of computer vision models. In this paper, we propose Auxiliary Fourier-basis Augmentation (AFA), a complementary technique targeting augmentation in the frequency domain and filling the augmentation gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions, OOD generalization, and consistency of performance of models against increasing perturbations, with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Code and models can be found at: https://github.com/nis-research/afa-augment