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

• URL: http://arxiv.org/abs/2403.01944v2
• Date: Tue, 5 Mar 2024 08:43:31 GMT
• Title: Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
• Authors: Puru Vaish, Shunxin Wang and Nicola Strisciuglio
• Abstract summary: Auxiliary Fourier-basis Augmentation (AFA) is a technique targeting augmentation in the frequency domain and filling the augmentation gap left by visual augmentations. 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.
• Score: 3.129187821625805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

Related papers

• Visual Fourier Prompt Tuning [63.66866445034855]
  We propose the Visual Fourier Prompt Tuning (VFPT) method as a general and effective solution for adapting large-scale transformer-based models. Our approach incorporates the Fast Fourier Transform into prompt embeddings and harmoniously considers both spatial and frequency domain information. Our results demonstrate that our approach outperforms current state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2024-11-02T18:18:35Z)
• Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis [64.46312434121455]
  Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. We propose task-specific FAS data augmentation (FAS-Aug), which increases data diversity by synthesizing data of artifacts. We also propose Spoofing Attack Risk Equalization (SARE) to prevent models from relying on certain types of artifacts and improve the generalization performance.
  arXiv  Detail & Related papers  (2024-09-04T01:45:18Z)
• Data Augmentation for Traffic Classification [54.92823760790628]
  Data Augmentation (DA) is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks. DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks.
  arXiv  Detail & Related papers  (2024-01-19T15:25:09Z)
• DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection [77.6648187359111]
  We propose a novel data augmentation method, named textbfDualAug, to keep the augmentation in distribution as much as possible at a reasonable time and computational cost. Experiments on supervised image classification benchmarks show that DualAug improve various automated data augmentation method.
  arXiv  Detail & Related papers  (2023-10-12T08:55:10Z)
• Frequency Compensated Diffusion Model for Real-scene Dehazing [6.105813272271171]
  We consider a dehazing framework based on conditional diffusion models for improved generalization to real haze. The proposed dehazing diffusion model significantly outperforms state-of-the-art methods on real-world images.
  arXiv  Detail & Related papers  (2023-08-21T06:50:44Z)
• Tied-Augment: Controlling Representation Similarity Improves Data Augmentation [18.446051824487792]
  We propose a framework called Tied-Augment to improve data augmentation in a wide range of applications. Tied-Augment can improve state-of-the-art methods from data augmentation (e.g. RandAugment, mixup), optimization (e.g. SAM), and semi-supervised learning (e.g. FixMatch)
  arXiv  Detail & Related papers  (2023-05-22T22:23:40Z)
• Soft Augmentation for Image Classification [68.71067594724663]
  We propose generalizing augmentation with invariant transforms to soft augmentation. We show that soft targets allow for more aggressive data augmentation. We also show that soft augmentations generalize to self-supervised classification tasks.
  arXiv  Detail & Related papers  (2022-11-09T01:04:06Z)
• Augmentation-induced Consistency Regularization for Classification [25.388324221293203]
  We propose a consistency regularization framework based on data augmentation, called CR-Aug. CR-Aug forces the output distributions of different sub models generated by data augmentation to be consistent with each other. We implement CR-Aug to image and audio classification tasks and conduct extensive experiments to verify its effectiveness.
  arXiv  Detail & Related papers  (2022-05-25T03:15:36Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.