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We propose a multigrid extension of convolutional neural networks (CNNs).
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
B. A. Olshausen, C. H. Anderson, and D. C. V. Essen · 1993
Earlier work this paper cites.
Neural abstraction pyramid: A hierarchical image understanding architecture
S. Behnke and R. Rojas · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Hierarchy and adaptivity in segmenting visual scenes
E. Sharon, M. Galun, D. Sharon, R. Basri, and A. Brandt · 2006
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
Earlier work this paper cites.
Progressive multigrid eigensolvers for multiscale spectral segmentation
M. Maire and S. X. Yu · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
SegNet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
Earlier work this paper cites.
Compressing neural networks with the hashing trick
W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen · 2015
Earlier work this paper cites.
BinaryConnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Bottom-up and top-down reasoning with hierarchical rectified gaussians
P. Hu and D. Ramanan · 2016
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Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2016
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Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
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Stacked hourglass networks for human pose estimation
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O. Ronneberger, P. Fischer, and T. Brox · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Highway networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Deep fried convnets
Z. Yang, M. Moczulski, M. Denil, N. de Freitas, A. J. Smola, L. Song, and Z. Wang · 2015
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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A. Newell, K. Yang, and J. Deng · 2016
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XNOR-Net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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Wide residual networks
S. Zagoruyko and N. Komodakis · 2016
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Multi-scale dense convolutional networks for efficient prediction
G. Huang, D. Chen, T. Li, F. Wu, L. van der Maaten, and K. Q. Weinberger · 2017
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FractalNet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2017
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Deciding how to decide: Dynamic routing in artificial neural networks
M. McGill and P. Perona · 2017
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