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Data augmentation, a technique in which a training set is expanded with class-preserving transformations, is ubiquitous in modern machine learning pipelines.
Creating artificial neural networks that generalize
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Tangent prop-a formalism for specifying selected invariances in an adaptive network
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Training with noise is equivalent to tikhonov regularization
Bishop, C. M · 1995
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From data distributions to regularization in invariant learning
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Incorporating invariances in support vector learning machines
Schölkopf, B., Burges, C., and Vapnik, V · 1996
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Transformation invariance in pattern recognition—tangent distance and tangent propagation
Simard, P., LeCun, Y., Denker, J., and Victorri, B · 1998
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Geometry and invariance in kernel based methods
Burges, C. J. C · 1999
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The digital database for screening mammography
Heath, M., Bowyer, K., Kopans, D., Moore, R., and Kegelmeyer, P · 2000
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Vicinal risk minimization
Chapelle, O., Weston, J., Bottou, L., and Vapnik, V · 2001
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On kernel-target alignment
Cristianini, N., Shawe-Taylor, J., Elisseeff, A., and Kandola, J. S · 2002
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Training invariant support vector machines
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A distribution-free theory of nonparametric regression
Györfi, L., Kohler, M., Krzyzak, A., and Walk, H · 2006
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Enhancing text categorization with semantic-enriched representation and training data augmentation
Lu, X., Zheng, B., Velivelli, A., and Zhai, C · 2006
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Invariant kernel functions for pattern analysis and machine learning
Haasdonk, B. and Burkhardt, H · 2007
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Random features for large-scale kernel machines
Rahimi, A. and Recht, B · 2007
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Convex learning with invariances
Teo, C. H., Globerson, A., Roweis, S. T., and Smola, A. J · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Empirical Bernstein bounds and sample variance penalization
Maurer, A. and Pontil, M · 2009
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Deep, big, simple neural nets for handwritten digit recognition
Cireşan, D. C., Meier, U., Gambardella, L. M., and Schmidhuber, J · 2010
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The manifold tangent classifier
Rifai, S., Dauphin, Y. N., Vincent, P., Bengio, Y., and Muller, X · 2011
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Curated breast imaging subset of ddsm
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Namkoong, H. and Duchi, J · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
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Kernel mean embedding of distributions: A review and beyond
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Variance-based regularization with convex objectives
Namkoong, H. and Duchi, J. C · 2017
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Learning from distributions via support measure machines
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The cancer imaging archive (TCIA): maintaining and operating a public information repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al · 2013
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
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Graham, B · 2014
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Invariant backpropagation: how to train a transformation-invariant neural network
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Learning with group invariant features: A kernel perspective
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Local group invariant representations via orbit embeddings
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Learning to compose domain-specific transformations for data augmentation
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Improving music source separation based on deep neural networks through data augmentation and network blending
Uhlich, S., Porcu, M., Giron, F., Enenkl, M., Kemp, T., Takahashi, N., and Mitsufuji, Y · 2017
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Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
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Marginalized CNN: Learning deep invariant representations
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Autoaugment: Learning augmentation policies from data
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Nonparametric regression and classification, 2018
Tibshirani, R. and Wasserman, L · 2018
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Learning invariances using the marginal likelihood
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