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We introduce an architecture for large-scale image categorization that enables the end-to-end learning of separate visual features for the different classes to distinguish.
ICDAR 2003 robust reading competitions
S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, and R. Young · 2003
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images, 2009
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End-to-end scene text recognition
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Imagenet classification with deep convolutional neural networks
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ICDAR 2013 robust reading competition
D. Karatzas, F. Shafait, S. Uchida, M. Iwamura, L. G. i Bigorda, S. R. Mestre, J. Mas, D. F. Mota, J. Almazán, and L. de las Heras · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
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Synthetic data and artificial neural networks for natural scene text recognition
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Autoencoder trees
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J. Long, E. Shelhamer, and T. Darrell · 2015
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Going deeper with convolutions
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HD-CNN: hierarchical deep convolutional neural networks for large scale visual recognition
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Network of experts for large-scale image categorization
K. Ahmed, M. H. Baig, and L. Torresani · 2016
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Expert gate: Lifelong learning with a network of experts
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Deep neural decision forests
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