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Fourier Transform Approximation as an Auxiliary Task for Image Classification

arXiv 2106.11478 ↗First posted 22 Jun 2021cs.CVcited by 0

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Abstract (arXiv)

Image reconstruction is likely the most predominant auxiliary task for image classification, but we would like to think twice about this convention. In this paper, we investigated "approximating the Fourier Transform of the input image" as a potential alternative, in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction. We experimented with five popular classification architectures on the CIFAR-10 dataset, and the empirical results indicated that our proposed auxiliary task generally improves the classification accuracy. More notably, the results showed that in certain cases our proposed auxiliary task may enhance the classifiers' resistance to adversarial attacks generated using the fast gradient sign method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 22 Jun 2021.

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References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  5. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
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  7. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  8. 2020Fast is better than free: Revisiting adversarial trainingcited by 727
  9. 2018Auxiliary Tasks in Multi-task Learningcited by 108
  10. 2019Self-Supervised Generalisation with Meta Auxiliary Learningcited by 88
  11. 2021SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domaincited by 30
  12. 2020Towards Frequency-Based Explanation for Robust CNNcited by 29
2 more
  1. 2016Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classificationcited by 18
  2. 2018Regularizing Deep Networks by Modeling and Predicting Label Structurecited by 9

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