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Big neural networks trained on large datasets have advanced the state-of-the-art for a large variety of challenging problems, improving performance by a large margin.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
T. Tieleman and G. Hinton · 2012
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
A. M. Saxe, J. L. McClelland, and S. Ganguli · 2013
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On the importance of initialization and momentum in deep learning
I. Sutskever, J. Martens, G. Dahl, and G. Hinton · 2013
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2d human pose estimation: New benchmark and state of the art analysis
M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele · 2014
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Training deep neural networks with low precision multiplications
M. Courbariaux, Y. Bengio, and J.-P. David · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Joint training of a convolutional network and a graphical model for human pose estimation
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 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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Fixed point quantization of deep convolutional networks
D. D. Lin, S. S. Talathi, and V. S. Annapureddy · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2015
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Human pose estimation via convolutional part heatmap regression
A. Bulat and G. Tzimiropoulos · 2016
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Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1
M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Multi-context attention for human pose estimation
X. Chu, W. Yang, W. Ouyang, C. Ma, A. L. Yuille, and X. Wang · 2017
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Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Towards accurate multi-person pose estimation in the wild
G. Papandreou, T. Zhu, N. Kanazawa, A. Toshev, J. Tompson, C. Bregler, and K. Murphy · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Learning feature pyramids for human pose estimation
W. Yang, S. Li, W. Ouyang, H. Li, and X. Wang · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
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Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Stacked hourglass networks for human pose estimation
A. Newell, K. Yang, and J. Deng · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Convolutional pose machines
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh · 2016
Cited alongside, same era.
Quantized convolutional neural networks for mobile devices
J. Wu, C. Leng, Y. Wang, Q. Hu, and J. Cheng · 2016
Cited alongside, same era.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
Cited alongside, same era.
S. Zagoruyko and N. Komodakis · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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Hierarchical binary cnns for landmark localization with limited resources
A. Bulat and Y. Tzimiropoulos · 2018
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Detect-and-track: Efficient pose estimation in videos
R. Girdhar, G. Gkioxari, L. Torresani, M. Paluri, and D. Tran · 2018
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Multi-scale structure-aware network for human pose estimation
L. Ke, M.-C. Chang, H. Qi, and S. Lyu · 2018
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Deeply learned compositional models for human pose estimation
W. Tang, P. Yu, and Y. Wu · 2018
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Quantized densely connected u-nets for efficient landmark localization
Z. Tang, X. Peng, S. Geng, L. Wu, S. Zhang, and D. Metaxas · 2018
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Clip-q: Deep network compression learning by in-parallel pruning-quantization
F. Tung and G. Mori · 2018
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Simple baselines for human pose estimation and tracking
B. Xiao, H. Wu, and Y. Wei · 2018
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Explicit loss-error-aware quantization for low-bit deep neural networks
A. Zhou, A. Yao, K. Wang, and Y. Chen · 2018
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