## AutoAugment

swMATH ID: | 39069 |

Software Authors: | Cubuk, Ekin D.; Zoph, Barret; Mane, Dandelion; Vasudevan, Vijay; Le, Quoc V. |

Description: | AutoAugment: Learning Augmentation Policies from Data. Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch. A sub-policy consists of two operations, each operation being an image processing function such as translation, rotation, or shearing, and the probabilities and magnitudes with which the functions are applied. We use a search algorithm to find the best policy such that the neural network yields the highest validation accuracy on a target dataset. Our method achieves state-of-the-art accuracy on CIFAR-10, CIFAR-100, SVHN, and ImageNet (without additional data). On ImageNet, we attain a Top-1 accuracy of 83.5 |

Homepage: | https://paperswithcode.com/paper/autoaugment-learning-augmentation-policies |

Related Software: | mixup; CIFAR; ImageNet; Adam; PyTorch; MixMatch; RandAugment; AlexNet; FixMatch; GitHub; MNIST; SGDR; Faster R-CNN; MS-COCO; TensorFlow; Python; Entropy-SGD; EfficientNet; SMOTE; PyGlove |

Cited in: | 10 Documents |

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### Cited by 40 Authors

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### Cited in 9 Serials

### Cited in 5 Fields

8 | Computer science (68-XX) |

3 | Statistics (62-XX) |

2 | Operations research, mathematical programming (90-XX) |

1 | Biology and other natural sciences (92-XX) |

1 | Systems theory; control (93-XX) |