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1 Introduction 1 2 Unsupervised Data Augmentation (UDA) 2 2.1 Background: Supervised Data Augmentation 2 2.2 Unsupervised Data Augmentation 3 2.3 Augmentation Strategies for Different Tasks 4 2.4 Trade-off Between Diversity and Validity for Data Augmentation 5 3 Additional Training Techniques 5 3.1 Training Signal Annealing 6 3.2 Sharpening Predictions 7 3.3 Domain-relevance Data Filtering 7 4 Experiments 7 4.1 Text Classification Experiments 7 4.2 Comparison with semi-supervised learning methods 9 4.3 ImageNet experiments 10 4.4 Ablation Studies 10 5 Related Work 12 6 Conclusion 12 A Complete Related Work 17 B Details for TF-IDF based word replacing for Text Classification 17 C Additional Results on CIFAR-10 and SVHN 18 C.1 CIFAR-10 with 4,000 examples and SVHN with 1,000 examples 18 C.2 Results with different labeled set sizes 18 D Experiment Details 19 D.1 Text Classifications 19 D.2 CIFAR-10 and SVHN 19 D.3 ImageNet 20

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LaTeX with hyperref package

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ZH