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2020

UniformAugment: A Search-free Probabilistic Data Augmentation Approach

LingChen, Tom Ching, Khonsari, Ava, Lashkari, Amirreza et al.

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Augmenting training datasets has been shown to improve the learning effectiveness for several computer vision tasks.

A good augmentation produces an augmented dataset that adds variability while retaining the statistical properties of the original dataset.
Some techniques, such as AutoAugment and Fast AutoAugment, have introduced a search phase to find a set of suitable augmentation policies for a given model and dataset.
This comes at the cost of great computational overhead, adding up to several thousand GPU hours.

UniformAugment: A Search-free Probabilistic Data Augmentation Approach · Around

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Ho, D., Liang, E., Stoica, I., Abbeel, P., Chen, X.: Population based augmentation: Efficient learning of augmentation policy schedules. arXiv:1905.05393 [cs.CV] (2019)
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Hoffer, E., Ben-Nun, T., Hubara, I., Giladi, N., Hoefler, T., Soudry, D.: Augment your batch: better training with larger batches. arXiv:1901.09335 [cs.LG] (2019)
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Lim, S., Kim, I., Kim, T., Kim, C., Kim, S.: Fast autoaugment. In: Advances in Neural Information Processing Systems (NeurIPS) 32. pp. 6662–6672 (2019)
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Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 113–123 (2019)

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Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: RandAugment: Practical automated data augmentation with a reduced search space. arXiv:1909.13719 [cs.CV] (2019)

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Zhang, X., Wang, Q., Zhang, J., Zhong, Z.: Adversarial autoaugment. arXiv:1912.11188 [cs.CV] (2019)

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