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The recent WSNet [1] is a new model compression method through sampling filterweights from a compact set and has demonstrated to be effective for 1D convolutionneural networks (CNNs).
Network slimming by slimmable networks: Towards one-shot architecture search for channel numbers
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Universally slimmable networks and improved training techniques
Jiahui Yu and Thomas Huang · 1903
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Summed-area tables for texture mapping
Franklin C Crow · 1984
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Rapid object detection using a boosted cascade of simple features
Paul Viola, Michael Jones, et al · 2001
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Epitomic analysis of appearance and shape
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Sparse and redundant modeling of image content using an image-signature-dictionary
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Video epitomes
Vincent Cheung, Brendan J Frey, and Nebojsa Jojic · 2008
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Memory bounded deep convolutional networks
Maxwell D Collins and Pushmeet Kohli · 2014
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Speeding up convolutional neural networks with low rank expansions
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Very deep convolutional networks for large-scale image recognition
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Esc: Dataset for environmental sound classification
Karol J Piczak · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Eie: efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A Horowitz, and William J Dally · 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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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 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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Igcv3: Interleaved low-rank group convolutions for efficient deep neural networks
Ke Sun, Mingjie Li, Dong Liu, and Jingdong Wang · 2018
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Wenling Shang, Kihyuk Sohn, Diogo Almeida, and Honglak Lee · 2016
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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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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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Wsnet: Compact and efficient networks through weight sampling
Xiaojie Jin, Yingzhen Yang, Ning Xu, Jianchao Yang, Nebojsa Jojic, Jiashi Feng, and Shuicheng Yan · 2017
Cited alongside, same era.
Swish: a self-gated activation function
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
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Chamnet: Towards efficient network design through platform-aware model adaptation
Xiaoliang Dai, Peizhao Zhang, Bichen Wu, Hongxu Yin, Fei Sun, Yanghan Wang, Marat Dukhan, Yunqing Hu, Yiming Wu, Yangqing Jia, et al · 2018
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Channelnets: Compact and efficient convolutional neural networks via channel-wise convolutions
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
Cited alongside, same era.
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V Le · 2018
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Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search
Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Yanghan Wang, Fei Sun, Yiming Wu, Yuandong Tian, Peter Vajda, Yangqing Jia, and Kurt Keutzer · 2018
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Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang · 2018
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
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Partial order pruning: for best speed/accuracy trade-off in neural architecture search
Xin Li, Yiming Zhou, Zheng Pan, and Jiashi Feng · 2019
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Asap: Architecture search, anneal and prune
Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, and Lihi Zelnik-Manor · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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