mlsanityData to 7 Oct 2026

Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation

arXiv 2209.02369 ↗First posted 6 Sep 2022cs.CVcited by 4

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Abstract (arXiv)

Although Convolutional Neural Networks (CNNs) have high accuracy in image recognition, they are vulnerable to adversarial examples and out-of-distribution data, and the difference from human recognition has been pointed out. In order to improve the robustness against out-of-distribution data, we present a frequency-based data augmentation technique that replaces the frequency components with other images of the same class. When the training data are CIFAR10 and the out-of-distribution data are SVHN, the Area Under Receiver Operating Characteristic (AUROC) curve of the model trained with the proposed method increases from 89.22% to 98.15%, and further increased to 98.59% when combined with another data augmentation method. Furthermore, we experimentally demonstrate that the robust model for out-of-distribution data uses a lot of high-frequency components of the image.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 6 Sep 2022.

Builds on

16 of 21 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
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  12. 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
4 more
  1. 2020Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Datacited by 408
  2. 2019High Frequency Component Helps Explain the Generalization of Convolutional Neural Networkscited by 310
  3. 2019A Fourier Perspective on Model Robustness in Computer Visioncited by 288
  4. 2021Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domaincited by 70

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Cited by year

Citing papers per year: 2023: 3; 2024: 1.

4 papers.

  1. 2023HybridAugment++: Unified Frequency Spectra Perturbations for Model Robustnesscited by 12
  2. 2023Exploiting Frequency Spectrum of Adversarial Images for General Robustnesscited by 0
  3. 2023Information Fusion for Assistance Systems in Production Assessmentcited by 0
  4. 2024Image edge enhancement for effective image classificationcited by 0