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We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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cudnn: Efficient primitives for deep learning
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Fast algorithms for convolutional neural networks
Andrew Lavin and Scott Gray · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Residual networks behave like ensembles of relatively shallow networks
Andreas Veit, Michael J Wilber, and Serge Belongie · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2016
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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Diracnets: Training very deep neural networks without skip-connections
Sergey Zagoruyko and Nikos Komodakis · 2017
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Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
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Approximated oracle filter pruning for destructive cnn width optimization
Xiaohan Ding, Guiguang Ding, Yuchen Guo, Jungong Han, and Chenggang Yan · 2019
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Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks
Xiaohan Ding, Yuchen Guo, Guiguang Ding, and Jungong Han · 2019
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Bag of tricks for image classification with convolutional neural networks
Tong He, Zhi Zhang, Hang Zhang, Zhongyue Zhang, Junyuan Xie, and Mu Li · 2019
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Searching for mobilenetv3
Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, and Yukun Zhu · 2019
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Pyramid scene parsing network
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2017
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Auto-balanced filter pruning for efficient convolutional neural networks
Xiaohan Ding, Guiguang Ding, Jungong Han, and Sheng Tang · 2018
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Progressive neural architecture search
Chenxi Liu, Barret Zoph, Maxim Neumann, Jonathon Shlens, Wei Hua, Li-Jia Li, Li Fei-Fei, Alan Yuille, Jonathan Huang, and Kevin Murphy · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel Schoenholz, and Jeffrey Pennington · 2018
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Aaditya Prakash, James A. Storer, Dinei A. F. Florêncio, and Cha Zhang · 2019
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Pytorch example
PyTorch · 2019
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Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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Do-conv: Depthwise over-parameterized convolutional layer
Jinming Cao, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, and Changhe Tu · 2020
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Expandnets: Linear over-parameterization to train compact convolutional networks
Shuxuan Guo, Jose M Alvarez, and Mathieu Salzmann · 2020
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Intel mkl
Intel · 2020
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Going deeper with neural networks without skip connections
Oyebade K Oyedotun, Djamila Aouada, Björn Ottersten, et al · 2020
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Designing network design spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, and Piotr Dollár · 2020
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