mlsanityData to 7 Oct 2026

Saliency Map Based Data Augmentation

arXiv 2205.14686 ↗First posted 29 May 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Data augmentation is a commonly applied technique with two seemingly related advantages. With this method one can increase the size of the training set generating new samples and also increase the invariance of the network against the applied transformations. Unfortunately all images contain both relevant and irrelevant features for classification therefore this invariance has to be class specific. In this paper we will present a new method which uses saliency maps to restrict the invariance of neural networks to certain regions, providing higher test accuracy in classification tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 29 May 2022.

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20 of 27 references.

References · 20

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  12. 2017SmoothGrad: removing noise by adding noisecited by 1,097
8 more
  1. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  2. 2019AugMix: A Simple Data Processing Method to Improve Robustness and Uncertaintycited by 897
  3. 2019Making Convolutional Networks Shift-Invariant Againcited by 390
  4. 2018Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomizationcited by 292
  5. 2018Why do deep convolutional networks generalize so poorly to small image transformations?cited by 198
  6. 2020SuperMix: Supervising the Mixing Data Augmentationcited by 58
  7. 2020KeepAugment: A Simple Information-Preserving Data Augmentation Approachcited by 52
  8. 2019Learning robust visual representations using data augmentation invariancecited by 5

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