Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data

David Lowell
David Lowell
Brian E. Howard
Brian E. Howard
Byron C. Wallace
Byron C. Wallace
Cited by: 0|Views13

Abstract:

Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation. While UDA has gained popularity for text classification, open questions li...More

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