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Network pruning has been known to produce compact models without much accuracy degradation.
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Rakin, A. S.; He, Z.; Yang, L.; Wang, Y.; Wang, L.; and Fan, D. 2019 · 1905
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Optimal brain damage
LeCun, Y.; Denker, J. S.; and Solla, S. A. 1990 · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
Hassibi, B.; and Stork, D. G. 1993 · 1993
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Dinh, T.; Wang, B.; Bertozzi, A. L.; and Osher, S. J. 2020 · 2003
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Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Li, B.; Wang, S.; Jia, Y.; Lu, Y.; Zhong, Z.; Carin, L.; and Jana, S. 2020 · 2003
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Learning both weights and connections for efficient neural network
Han, S.; Pool, J.; Tran, J.; and Dally, W. 2015 · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
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Measuring neural net robustness with constraints
Bastani, O.; Ioannou, Y.; Lampropoulos, L.; Vytiniotis, D.; Nori, A.; and Criminisi, A. 2016 · 2016
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Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding
Han, S.; Mao, H.; and Dally, W. J. 2016 · 2016
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Deep Residual Learning for Image Recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Formal guarantees on the robustness of a classifier against adversarial manipulation
Hein, M.; and Andriushchenko, M. 2017 · 2017
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Densely Connected Convolutional Networks
Huang, G.; Liu, Z.; van der Maaten, L.; and Weinberger, K. Q. 2017 · 2017
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Adversarial Machine Learning at Scale
Kurakin, A.; Goodfellow, I. J.; and Bengio, S. 2017 · 2017
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Pruning Filters for Efficient ConvNets
Li, H.; Kadav, A.; Durdanovic, I.; Samet, H.; and Graf, H. P. 2017 · 2017
Cited alongside, same era.
Learning Efficient Convolutional Networks through Network Slimming
Liu, Z.; Li, J.; Shen, Z.; Huang, G.; Yan, S.; and Zhang, C. 2017 · 2017
Cited alongside, same era.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Luo, J.-H.; Wu, J.; and Lin, W. 2017 · 2017
Cited alongside, same era.
Stochastic activation pruning for robust adversarial defense
Dhillon, G. S.; Azizzadenesheli, K.; Lipton, Z. C.; Bernstein, J.; Kossaifi, J.; Khanna, A.; and Anandkumar, A. 2018 · 2018
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Sparse dnns with improved adversarial robustness
Guo, Y.; Zhang, C.; Zhang, C.; and Chen, Y. 2018 · 2018
Cited alongside, same era.
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Gui, S.; Wang, H. N.; Yang, H.; Yu, C.; Wang, Z.; and Liu, J. 2019 · 2019
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Rethinking the Value of Network Pruning
Liu, Z.; Sun, M.; Zhou, T.; Huang, G.; and Darrell, T. 2019 · 2019
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Adversarial Neural Pruning with Latent Vulnerability Suppression
Madaan, D.; Shin, J.; and Hwang, S. J. 2019 · 2019
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A convex relaxation barrier to tight robustness verification of neural networks
Salman, H.; Yang, G.; Zhang, H.; Hsieh, C.-J.; and Zhang, P. 2019 · 2019
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Towards Compact and Robust Deep Neural Networks
Sehwag, V.; Wang, S.; Mittal, P.; and Jana, S. 2019 · 2019
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ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
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Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden . ijcai.org
Lang, J., ed. 2018 · 2018
Cited alongside, same era.
Learning Sparse Neural Networks through L 0 L_{0} Regularization
Louizos, C.; Welling, M.; and Kingma, D. P. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Adversarial robustness of pruned neural networks
Wang, L.; Ding, G. W.; Huang, R.; Cao, Y.; and Lui, Y. C. 2018 · 2018
Cited alongside, same era.
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
Ye, J.; Lu, X.; Lin, Z.; and Wang, J. Z. 2018 · 2018
Cited alongside, same era.
Zhao, Y.; Shumailov, I.; Mullins, R.; and Anderson, R. 2018 · 2018
Cited alongside, same era.
The Search for Sparse, Robust Neural Networks
Cosentino, J.; Zaiter, F.; Pei, D.; and Zhu, J. 2019 · 2019
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Wang, B.; Shi, Z.; and Osher, S. 2019 · 2019
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ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
Yang, Y.; Zhang, G.; Xu, Z.; and Katabi, D. 2019 · 2019
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Adversarial Robustness vs Model Compression, or Both?
Ye, S.; Xu, K.; Liu, S.; Cheng, H.; Lambrechts, J.-H.; Zhang, H.; Zhou, A.; Ma, K.; Wang, Y.; and Lin, X. 2019 · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E. P.; Ghaoui, L. E.; and Jordan, M. I. 2019 · 2019
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Interpreting Adversarially Trained Convolutional Neural Networks
Zhang, T.; and Zhu, Z. 2019 · 2019
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Adversarially Robust Distillation
Goldblum, M.; Fowl, L.; Feizi, S.; and Goldstein, T. 2020 · 2020
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When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks
Guo, M.; Yang, Y.; Xu, R.; Liu, Z.; and Lin, D. 2020 · 2020
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Learning structured sparsity in deep neural networks
Wen, W.; Wu, C.; Wang, Y.; Chen, Y.; and Li, H. 2016 · 2082
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