Region Mixup
Not on the map. Why?
Abstract (arXiv)
This paper introduces a simple extension of mixup (Zhang et al., 2018) data augmentation to enhance generalization in visual recognition tasks. Unlike the vanilla mixup method, which blends entire images, our approach focuses on combining regions from multiple images.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 23 Sep 2024.
Builds on
11 of 13 references.
References · 11
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
- 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
- 2015Learning Deep Features for Discriminative Localizationcited by 2,430
- 2016Identity Mappings in Deep Residual Networkscited by 1,960
- 2017Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networkscited by 936
- 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
- 2018MixUp as Locally Linear Out-Of-Manifold Regularizationcited by 158
- 2020Torchattacks: A PyTorch Repository for Adversarial Attackscited by 156
- 2021Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversitycited by 127
- 2021AutoMix: Unveiling the Power of Mixup for Stronger Classifierscited by 69
Built on by
Citing papers per year: 2026: 1.
1 paper.