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Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations.
Rotation-invariant texture classification using modified gabor filters
G. M. Haley and B. S. Manjunath · 1995
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Object recognition from local scale-invariant features
D. G. Lowe · 1999
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The Art of Data Augmentation
D. a. van Dyk and X.-L. Meng · 2001
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
T. Ojala, M. Pietikäinen, and T. Mäenpää · 2002
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Handwritten digit recognition: benchmarking of state-of-the-art techniques
C. Liu, K. Nakashima, H. Sako, and H. Fujisawa · 2003
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Semi-local affine parts for object recognition
S. Lazebnik, C. Schmid, and J. Ponce · 2004
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Histograms of oriented gradients for human detection
N. Dalal and B. Triggs · 2005
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Face description with local binary patterns: Application to face recognition
T. Ahonen, A. Hadid, and M. Pietikäinen · 2006
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On the use of SIFT features for face authentication
M. Bicego, A. Lagorio, E. Grosso, and M. Tistarelli · 2006
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Multi-view stereo for community photo collections
M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S. M. Seitz · 2007
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Rotation-invariant and scale-invariant gabor features for texture image retrieval
J. Han and K. Ma · 2007
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An empirical evaluation of deep architectures on problems with many factors of variation
H. Larochelle, D. Erhan, A. C. Courville, J. Bergstra, and Y. Bengio · 2007
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Visualizing Data using t-SNE
L. Van Der Maaten and G. Hinton · 2008
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
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Visualizing higher-layer features of a deep network
D. Erhan, Y. Bengio, A. Courville, and P. Vincent · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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WLD: A robust local image descriptor
J. Chen, S. Shan, C. He, G. Zhao, M. Pietikäinen, X. Chen, and W. Gao · 2010
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Evaluation of pooling operations in convolutional architectures for object recognition
D. Scherer, A. C. Müller, and S. Behnke · 2010
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Transformation equivariant boltzmann machines
J. J. Kivinen and C. K. I. Williams · 2011
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Learning rotation-aware features: From invariant priors to equivariant descriptors
U. Schmidt and S. Roth · 2012
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Circular fourier-hog features for rotation invariant object detection in biomedical images
H. Skibbe and M. Reisert · 2012
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Learning invariant representations with local transformations
K. Sohn and H. Lee · 2012
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Oriented edge forests for boundary detection
S. Hallman and C. C. Fowlkes · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Deeply-supervised nets
C. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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ADADELTA: an adaptive learning rate method
M. D. Zeiler · 2012
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Invariant scattering convolution networks
J. Bruna and S. Mallat · 2013
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Rotation, scaling and deformation invariant scattering for texture discrimination
L. Sifre and S. Mallat · 2013
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Generative adversarial nets
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio · 2014
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Network In Network
M. Lin, Q. Chen, and S. Yan · 2014
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Rotation-invariant HOG descriptors using fourier analysis in polar and spherical coordinates
K. Liu, H. Skibbe, T. Schmidt, T. Blein, K. Palme, T. Brox, and O. Ronneberger · 2014
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R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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F. Wu, P. Hu, and D. Kong · 2015
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Rifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection
G. Cheng, P. Zhou, and J. Han · 2016
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Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
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Learning rotation invariant convolutional filters for texture classification
D. M. Gonzalez, M. Volpi, and D. Tuia · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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TI-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks
D. Laptev, N. Savinov, J. M. Buhmann, and M. Pollefeys · 2016
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Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
C. Lee, P. W. Gallagher, and Z. Tu · 2016
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Convolutional oriented boundaries
K. Maninis, J. Pont-Tuset, P. A. Arbeláez, and L. J. V. Gool · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Learning Deep Features for Discriminative Localization
B. Zhou, A. Khosla, L. A., A. Oliva, and A. Torralba · 2016
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