Summary of Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification, by Puru Vaish et al.
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification
by Puru Vaish, Shunxin Wang, Nicola Strisciuglio
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel computer vision technique called Auxiliary Fourier-basis Augmentation (AFA), which aims to enhance the robustness of models against unexpected changes in inputs. AFA fills the gap left by traditional visual augmentations and demonstrates its effectiveness in improving model performance against common corruptions, out-of-distribution generalization, and increasing perturbations. The technique can be seamlessly integrated with other augmentation methods to further boost performance. Experiments show that AFA yields consistent results, with negligible deficit to standard model performance. The authors provide open-source code and models at https://github.com/nis-research/afa-augment. This work contributes to the development of robust computer vision models for real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computer vision models more reliable when used in real-life situations. These models often perform poorly when they encounter unexpected data, which wasn’t accounted for during training. To solve this problem, scientists have developed a new technique called Auxiliary Fourier-basis Augmentation (AFA). AFA helps computer vision models become more robust and consistent by adding noise to the data in a specific way. The results show that AFA improves model performance when faced with unexpected data changes or corruptions. This is an important step towards creating reliable computer vision models for everyday use. |
Keywords
* Artificial intelligence * Generalization
