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The existing model compression methods via structured pruning typically require complicated multi-stage procedures.
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik · 2001
Earlier work this paper cites.
A half-space stochastic projected gradient method for group-sparsity regularization
Tianyi Chen, Guanyi Wang, Tianyu Ding, Bo Ji, Sheng Yi, and Zhihui Zhu · 2009
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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
A. Krizhevsky and G. Hinton · 2009
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On single image scale-up using sparse-representations
Roman Zeyde, Michael Elad, and Matan Protter · 2010
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Neural network compression via sparse optimization
Tianyi Chen, Bo Ji, Yixin Shi, Tianyu Ding, Biyi Fang, Sheng Yi, and Xiao Tu · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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A proximal stochastic gradient method with progressive variance reduction
Lin Xiao and Tong Zhang · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Single image super-resolution from transformed self-exemplars
Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja · 2015
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Deep feature synthesis: Towards automating data science endeavors
James Max Kanter and Kalyan Veeramachaneni · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 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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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
Earlier work this paper cites.
Ntire 2017 challenge on single image super-resolution: Dataset and study
Eirikur Agustsson and Radu Timofte · 2017
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A reduced-space algorithm for minimizing ℓ 1 \ell_{1} -regularized convex functions
Tianyi Chen, Frank E Curtis, and Daniel P Robinson · 2017
Earlier work this paper cites.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Luo, Jian-Hao and Wu, Jianxin and Lin, Weiyao · 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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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Fast bayesian optimization of machine learning hyperparameters on large datasets
Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, and Frank Hutter · 2017
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Bayesian compression for deep learning
Christos Louizos, Karen Ullrich, and Max Welling · 2017
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Structured bayesian pruning via log-normal multiplicative noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, and Dmitry P Vetrov · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Deephoyer: Learning sparser neural network with differentiable scale-invariant sparsity measures
Huanrui Yang, Wei Wen, and Hai Li · 2019
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Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks
Zhonghui You, Kun Yan, Jinmian Ye, Meng Ma, and Ping Wang · 2019
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Accelerate cnn via recursive bayesian pruning
Yuefu Zhou, Ya Zhang, Yanfeng Wang, and Qi Tian · 2019
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Neuron-level structured pruning using polarization regularizer
Tao Zhuang, Zhixuan Zhang, Yuheng Huang, Xiaoyi Zeng, Kai Shuang, and Xiang Li · 2019
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Orthant based proximal stochastic gradient method for ℓ 1 \ell_{1} -regularized optimization
Tianyi Chen, Tianyu Ding, Bo Ji, Guanyi Wang, Yixin Shi, Jing Tian, Sheng Yi, Xiao Tu, and Zhihui Zhu · 2020
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Fast, accurate, and lightweight super-resolution with cascading residual network
Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn · 2018
Cited alongside, same era.
Farsa for ℓ 1 \ell_{1} -regularized convex optimization: local convergence and numerical experience
Tianyi Chen, Frank E Curtis, and Daniel P Robinson · 2018
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Efficient multi-objective neural architecture search via lamarckian evolution
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
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Highly efficient salient object detection with 100k parameters
Shang-Hua Gao, Yong-Qiang Tan, Ming-Ming Cheng, Chengze Lu, Yunpeng Chen, and Shuicheng Yan · 2020
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Operation-aware soft channel pruning using differentiable masks
Minsoo Kang and Bohyung Han · 2020
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Fanxu Meng, Hao Cheng, Ke Li, Huixiang Luo, Xiaowei Guo, Guangming Lu, and Xing Sun · 2020
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Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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N2nskip: Learning highly sparse networks using neuron-to-neuron skip connections
Avinash Sharma, Arvind Subramaniam, and · 2020
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Bayesian bits: Unifying quantization and pruning
Mart van Baalen, Christos Louizos, Markus Nagel, Rana Ali Amjad, Ying Wang, Tijmen Blankevoort, and Max Welling · 2020
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Automatic neural network compression by sparsity-quantization joint learning: A constrained optimization-based approach
Haichuan Yang, Shupeng Gui, Yuhao Zhu, and Ji Liu · 2020
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Structured sparsity inducing adaptive optimizers for deep learning
Tristan Deleu and Yoshua Bengio · 2021
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Lossless cnn channel pruning via decoupling remembering and forgetting
Xiaohan Ding, Tianxiang Hao, Jianchao Tan, Ji Liu, Jungong Han, Yuchen Guo, and Guiguang Ding · 2021
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Learning pruning-friendly networks via frank-wolfe: One-shot, any-sparsity, and no retraining
Lu Miao, Xiaolong Luo, Tianlong Chen, Wuyang Chen, Dong Liu, and Zhangyang Wang · 2021
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Onnx2torch: an onnx to pytorch converter
Arseny · 2022
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On the channel pruning using graph convolution network for convolutional neural network acceleration
Yuan Cao Di Jiang and Qiang Yang · 2022
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Revisiting random channel pruning for neural network compression
Yawei Li, Kamil Adamczewski, Wen Li, Shuhang Gu, Radu Timofte, and Luc Van Gool · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Attentive fine-grained structured sparsity for image restoration
Junghun Oh, Heewon Kim, Seungjun Nah, Cheeun Hong, Jonghyun Choi, and Kyoung Mu Lee · 2022
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Towards Automatic Neural Architecture Search within General Super-Networks
Tianyi Chen, Luming Liang, Tianyu Ding, and Zharkov Ilya · 2023
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An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization
Yutong Dai, Tianyi Chen, Guanyi Wang, and Daniel Robinson · 2023
Closest in time.