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Data augmentation is a key element in training high-dimensional models.
Equation of state calculations by fast computing machines
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Tangent prop – a formalism for specifying selected invariances in an adaptive network
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Pet-ct image registration in the chest using free-form deformations
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Computational anatomy: shape, growth, and atrophy comparison via diffeomorphisms
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Scape: shape completion and animation of people
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Computing large deformation metric mappings via geodesic flows of diffeomorphisms
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Locus: Learning object classes with unsupervised segmentation
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A log-euclidean polyaffine framework for locally rigid or affine registration
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Semi-supervised learning
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Enhancing text categorization with semantic-enriched representation and training data augmentation
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Imagenet classification with deep convolutional neural networks
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Learning invariant representations with local transformations
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Locally orderless registration
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Unsupervised feature learning by augmenting single images
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Learning hierarchical features for scene labeling
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Vocal tract length perturbation (VTLP) improves speech recognition
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Intrinsic statistics on Riemannian manifolds: Basic tools for geometric measurements
X. Pennec · 2006
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Pop: Patchwork of parts models for object recognition
Y. Amit and A. Trouvé · 2007
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A new validation method for x-ray mammogram registration algorithms using a projection model of breast x-ray compression
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Training invariant support vector machines using selective sampling
G. Loosli, S. Canu, and L. Bottou · 2007
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Brownian warps for non-rigid registration
M. Nielsen, P. Johansen, A. Jackson, B. Lautrup, and S. Hauberg · 2008
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Measuring invariances in deep networks
I. Goodfellow, H. Lee, Q. V. Le, A. Saxe, and A. Y. Ng · 2009
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Learning invariant representations and applications to face verification
Q. Liao, J. Z. Leibo, and T. Poggio · 2013
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Faust: Dataset and evaluation for 3d mesh registration
F. Bogo, J. Romero, M. Loper, and M. J. Black · 2014
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Data augmentation for deep neural network acoustic modeling
X. Cui, V. Goel, and B. Kingsbury · 2014
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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Detecting objects using deformation dictionaries
B. Hariharan, C. L. Zitnick, and P. Dollár · 2014
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Locally scale-invariant convolutional neural networks
A. Kanazawa, A. Sharma, and D. Jacobs · 2014
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Highly-expressive spaces of well-behaved transformations: Keeping it simple
O. Freifeld, S. Hauberg, K. Batmanghelich, and J. W. F. III · 2015
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Data augmentation for machine learning redshifts applied to SDSS galaxies
B. Hoyle, M. M. Rau, C. Bonnett, S. Seitz, and J. Weller · 2015
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3D shapenets: A deep representation for volumetric shape modeling
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Finite-dimensional Lie algebras for fast diffeomorphic image registration
M. Zhang and P. T. Fletcher · 2015
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