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Recent advances in deep neural networks (DNNs) lead to tremendously growing network parameters, making the deployments of DNNs on platforms with limited resources extremely difficult.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Fast computation of wasserstein barycenters
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Iterative bregman projections for regularized transportation problems
Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, and Gabriel Peyré · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Optimal transport for domain adaptation
Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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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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Pruning convolutional neural networks for resource efficient inference
Tero Karras Timo Aila Jan Kautz Pavlo Molchanov, Stephen Tyree · 2017
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Pruning for efficient convnetspruning filters
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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More is less: A more complicated network with less inference complexity
Xuanyi Dong, Junshi Huang, Yi Yang, and Shuicheng Yan · 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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Multilevel clustering via wasserstein means
Nhat Ho, Xuan Long Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, and Dinh Phung · 2017
Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z. Wang · 2018
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A fast proximal point method for computing exact wasserstein distance
Yujia Xie, Xiangfeng Wang, Ruijia Wang, and Hongyuan Zha · 2018
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Decentralize and randomize: Faster algorithm for wasserstein barycenters
Pavel Dvurechenskii, Darina Dvinskikh, Alexander Gasnikov, Cesar Uribe, and Angelia Nedich · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
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Global sparse momentum sgd for pruning very deep neural networks
Xiaohan Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, Ji Liu, et al · 2019
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Fast discrete distribution clustering using wasserstein barycenter with sparse support
Jianbo Ye, Panruo Wu, James Z Wang, and Jia Li · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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“learning-compression” algorithms for neural net pruning
Miguel A Carreira-Perpinán and Yerlan Idelbayev · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
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Nisp: Pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis · 2018
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SNIP: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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Clustering, factor discovery and optimal transport
Hongkang Yang and Esteban G Tabak · 2019
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Learning entropic wasserstein embeddings
Charlie Frogner, Farzaneh Mirzazadeh, and Justin Solomon · 2019
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Hierarchical optimal transport for document representation
Mikhail Yurochkin, Sebastian Claici, Edward Chien, Farzaneh Mirzazadeh, and Justin M Solomon · 2019
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Interior-point methods strike back: Solving the wasserstein barycenter problem
Dongdong Ge, Haoyue Wang, Zikai Xiong, and Yinyu Ye · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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