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Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification.
Transformation invariance in pattern recognition-tangent distance and tangent propagation
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Imagenet classification with deep convolutional neural networks
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cissé, Yann N. Dauphin, and David Lopez-Paz. 2017 · 2017
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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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Kai Sheng Tai, Richard Socher, and Christopher Manning. 2015 · 2015
Cited alongside, same era.
That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using #petpeeve tweets
William Yang Wang and Diyi Yang. 2015 · 2015
Cited alongside, same era.
Aggregated learning: A vector quantization approach to learning with neural networks
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2018a
Cited in the paper.
Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2018b
Cited in the paper.
Url https://github.com/yoonkim/cnn_sentence
Kim. 2014a
Cited in the paper.
Convolutional neural networks for sentence classification
Yoon Kim. 2014b
Cited in the paper.
Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer
Vikas Verma, Alex Lamb, Christopher Beckham, Aaron C. Courville, Ioannis Mitliagkas, and Yoshua Bengio
Cited in the paper.
Data augmentation via dependency tree morphing for low-resource languages
Gözde Gül Sahin and Mark Steedman. 2018 · 2018
Later among the works it cites.