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Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.
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Robust principal component analysis?
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Flattened convolutional neural networks for feedforward acceleration
Jonghoon Jin, Aysegul Dundar, and Eugenio Culurciello · 2014
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Speeding-up convolutional neural networks using fine-tuned cp-decomposition
Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, and Victor Lempitsky · 2014
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Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Sparse convolutional neural networks
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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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Compressing deep neural networks using a rank-constrained topology
Preetum Nakkiran, Raziel Alvarez, Rohit Prabhavalkar, and Carolina Parada · 2015
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Convolutional neural networks with low-rank regularization
Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al · 2015
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Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding
Song Han, Huizi Mao, and William J Dally · 2015
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BinaryConnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Less is more: Towards compact CNNs
Hao Zhou, Jose M Alvarez, and Fatih Porikli · 2016
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Pruning filters for efficient convnets
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Learning structured sparsity in deep neural networks
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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XNOR-Net: ImageNet classification using binary convolutional neural networks
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Adversarial machine learning at scale
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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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EnergyNet: Energy-efficient dynamic inference
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Adversarial learning of portable student networks
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Deep k-means: Re-training and parameter sharing with harder cluster assignments for compressing deep convolutions
Junru Wu, Yue Wang, Zhenyu Wu, Zhangyang Wang, Ashok Veeraraghavan, and Yingyan Lin · 2018
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A unified framework of DNN weight pruning and weight clustering/quantization using ADMM
Shaokai Ye, Tianyun Zhang, Kaiqi Zhang, Jiayu Li, Jiaming Xie, Yun Liang, Sijia Liu, Xue Lin, and Yanzhi Wang · 2018
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DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 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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Sergey Zagoruyko and Nikos Komodakis · 2016
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A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2017
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On compressing deep models by low rank and sparse decomposition
Xiyu Yu, Tongliang Liu, Xinchao Wang, and Dacheng Tao · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Towards imperceptible and robust adversarial example attacks against neural networks
Bo Luo, Yannan Liu, Lingxiao Wei, and Qiang Xu · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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Stochastic activation pruning for robust adversarial defense
Guneet S Dhillon, Kamyar Azizzadenesheli, Zachary C Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, and Anima Anandkumar · 2018
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Defense against adversarial attacks using high-level representation guided denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Xiaolin Hu, and Jun Zhu · 2018
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2018
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Sparse DNNs with improved adversarial robustness
Yiwen Guo, Chao Zhang, Changshui Zhang, and Yurong Chen · 2018
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Yiren Zhao, Ilia Shumailov, Robert Mullins, and Ross Anderson · 2018
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Haichuan Yang, Yuhao Zhu, and Ji Liu · 2018
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Dual dynamic inference: Enabling more efficient, adaptive and controllable deep inference
Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan Nguyen, Richard Baraniuk, Zhangyang Wang, and Yingyan Lin · 2019
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Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images
Wuyang Chen, Ziyu Jiang, Zhangyang Wang, Kexin Cui, and Xiaoning Qian · 2019
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ECC: Platform-independent energy-constrained deep neural network compression via a bilinear regression model
Haichuan Yang, Yuhao Zhu, and Ji Liu · 2019
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Learning sparsity and quantization jointly and automatically for neural network compression via constrained optimization
Haichuan Yang, Shupeng Gui, Yuhao Zhu, and Ji Liu · 2019
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Adversarial robustness may be at odds with simplicity
Preetum Nakkiran · 2019
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Defensive quantization: When efficiency meets robustness
Ji Lin, Chuang Gan, and Song Han · 2019
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Adversarial robustness vs model compression, or both?
Shaokai Ye, Kaidi Xu, Sijia Liu, Hao Cheng, Jan-Henrik Lambrechts, Huan Zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, and Xue Lin · 2019
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