Data Augmentation

Posted on | In

  1. Traditional data augmentation

    • color, hue, illumination

    • flip, crop, shear, rotation, (piecewise) affine transformation, Cutout, RandErasing, HideAndSeek, GridMask

  2. Mixtures: Mixup [1], CutMix [2] (Mixture in spatial domain), GridMask [6], FMix [3] (Mixture in frequency)

  3. Learn optimal data augmentation strategy: [4] [5], AutoAugment, RandAugment, Fast AutoAugment, Faster AutoAugment, Greedy Augment.

  4. Semantic augmentation: [7]

A summary of existing data augmentation methods [link]

Reference

[1] mixup: Beyond empirical risk minimization

[2] Cutmix: Regularization strategy to train strong classifiers with localizable features

[3] Understanding and Enhancing Mixed Sample Data Augmentation

[4] AutoAugment: Learning Augmentation Strategies from Data

[5] The Effectiveness of Data Augmentation in Image Classification using Deep Learning

[6] GridMask Data Augmentation

[7] Regularizing Deep Networks with Semantic Data Augmentation