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arXiv:1709.01643 (stat)
[Submitted on 6 Sep 2017 (v1), last revised 30 Sep 2017 (this version, v3)]

Title:Learning to Compose Domain-Specific Transformations for Data Augmentation

Authors:Alexander J. Ratner, Henry R. Ehrenberg, Zeshan Hussain, Jared Dunnmon, Christopher RĂ©
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Abstract:Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically needed to achieve state-of-the-art results is a time-consuming manual task in practice. We propose a method for automating this process by learning a generative sequence model over user-specified transformation functions using a generative adversarial approach. Our method can make use of arbitrary, non-deterministic transformation functions, is robust to misspecified user input, and is trained on unlabeled data. The learned transformation model can then be used to perform data augmentation for any end discriminative model. In our experiments, we show the efficacy of our approach on both image and text datasets, achieving improvements of 4.0 accuracy points on CIFAR-10, 1.4 F1 points on the ACE relation extraction task, and 3.4 accuracy points when using domain-specific transformation operations on a medical imaging dataset as compared to standard heuristic augmentation approaches.
Comments: To appear at Neural Information Processing Systems (NIPS) 2017
Subjects: Machine Learning (stat.ML); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:1709.01643 [stat.ML]
  (or arXiv:1709.01643v3 [stat.ML] for this version)
  https://doi.org/10.48550/arXiv.1709.01643
arXiv-issued DOI via DataCite
Journal reference: Advances in Neural Information Processing Systems 30, 2017, 3236--3246

Submission history

From: Alexander Ratner [view email]
[v1] Wed, 6 Sep 2017 01:17:31 UTC (1,020 KB)
[v2] Sun, 17 Sep 2017 18:09:15 UTC (1,020 KB)
[v3] Sat, 30 Sep 2017 04:27:53 UTC (1,023 KB)
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