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Do deep nets really need to be deep?
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Instance normalization: The missing ingredient for fast stylization
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Do deep convolutional nets really need to be deep and convolutional?
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Semantic understanding of scenes through the ade20k dataset
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Learning deep architectures via generalized whitened neural networks
P. Luo · 2017
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Understanding deep learning requires rethinking generalization
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
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Pyramid scene parsing network
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollar, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Doll¨¢r, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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Mask r-cnn
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
S. Ioffe · 2017
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Eigennet: Towards fast and structural learning of deep neural networks
P. Luo · 2017
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H. Liu, K. Simonyan, and Y. Yang · 2018
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Differentiable learning-to-normalize via switchable normalization
P. Luo, J. Ren, Z. Peng, R. Zhang, and J. Li · 2018
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Towards understanding regularization in batch normalization
P. Luo, X. Wang, W. Shao, and Z. Peng · 2018
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Efficient neural architecture search via parameter sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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Batch kalman normalization: Towards training deep neural networks with micro-batches
G. Wang, J. Peng, P. Luo, X. Wang, and L. Lin · 2018
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Y. Wu and K. He · 2018
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