Corpus ID: 211532796

Understanding and Enhancing Mixed Sample Data Augmentation

@article{Harris2020UnderstandingAE,
  title={Understanding and Enhancing Mixed Sample Data Augmentation},
  author={E. Harris and A. Marcu and Matthew Painter and M. Niranjan and A. Pr{\"u}gel-Bennett and J. Hare},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.12047}
}
  • E. Harris, A. Marcu, +3 authors J. Hare
  • Published 2020
  • Computer Science
  • ArXiv
  • Mixed Sample Data Augmentation (MSDA) has received increasing attention in recent years, with many successful variants such as MixUp and CutMix. Following insight on the efficacy of CutMix in particular, we propose FMix, an MSDA that uses binary masks obtained by applying a threshold to low frequency images sampled from Fourier space. FMix improves performance over MixUp and CutMix for a number of state-of-theart models across a range of data sets and problem settings. We go on to analyse MixUp… CONTINUE READING
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