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Spatial Transformer Networks (STNs) estimate image transformations that can improve downstream tasks by `zooming in' on relevant regions in an image.
Traffic sign detection and classification around the world
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Document image defect models
Henry S Baird · 1992
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Bayesian interpolation
David JC MacKay · 1992
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Comparison of learning algorithms for handwritten digit recognition
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Best practices for convolutional neural networks applied to visual document analysis
Patrice Y Simard, Dave Steinkraus, and John C Platt · 2003
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Training invariant support vector machines using selective sampling
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Measuring invariances in deep networks
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Auto-encoding variational bayes
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Weight uncertainty in neural networks
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Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and Koray Kavukcuoglu · 2015
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Recurrent spatial transformer networks
Søren Kaae Sønderby, Casper Kaae Sønderby, Lars Maaløe, and Ole Winther · 2015
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
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Dreaming more data: Class-dependent distributions over diffeomorphisms for learned data augmentation
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Autoaugment: Learning augmentation strategies from data
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Reliable training and estimation of variance networks
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Diffeomorphic temporal alignment nets
Ron A Shapira Weber, Matan Eyal, Nicki Skafte, Oren Shriki, and Oren Freifeld · 2019
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Learning invariances in neural networks
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Randaugment: Practical automated data augmentation with a reduced search space
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On calibration of modern neural networks
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Simple and scalable predictive uncertainty estimation using deep ensembles
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The ucr time series classification archive, October 2018
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Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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