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The DenseNet architecture is highly computationally efficient as a result of feature reuse.
Torch7: A matlab-like environment for machine learning
R. Collobert, K. Kavukcuoglu, and C. Farabet · 2011
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cudnn: Efficient primitives for deep learning
S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
T. Chen, M. Li, Y. Li, M. Lin, N. Wang, M. Wang, T. Xiao, B. Xu, C. Zhang, and Z. Zhang · 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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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Training deep nets with sublinear memory cost
T. Chen, B. Xu, C. Zhang, and C. Guestrin · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun
Cited in the paper.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun
Cited in the paper.
Snapshot ensembles: Train 1, get m for free
G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger
Cited in the paper.
Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten
Cited in the paper.
https://github.com/pytorch
Pytorch · 2017
Closest in time.
Sgdr: stochastic gradient descent with restarts
I. Loshchilov and F. Hutter · 2017
Closest in time.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
Closest in time.
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