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Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,”
1998
Earlier work this paper cites.
A. Krizhevsky, G. Hinton
2009
Earlier work this paper cites.
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, “Reading digits in natural images with unsupervised feature learning,” 2011
2011
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,”
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,”
2015
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,” in
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” in
2016
Earlier work this paper cites.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in
2016
Earlier work this paper cites.
Y. Guo, A. Yao, and Y. Chen, “Dynamic network surgery for efficient dnns,” in
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in
2016
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: A simple and accurate method to fool deep neural networks,” in
2016
Earlier work this paper cites.
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf, “Pruning filters for efficient convnets,” in
2017
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in
2017
Earlier work this paper cites.
P. Chen, H. Zhang, Y. Sharma, J. Yi, and C. Hsieh, “ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen, “Incremental network quantization: Towards lossless cnns with low-precision weights,” in
2017
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in
2017
Earlier work this paper cites.
K. Neklyudov, D. Molchanov, A. Ashukha, and D. P. Vetrov, “Structured bayesian pruning via log-normal multiplicative noise,”
2017
Earlier work this paper cites.
T. DeVries and G. W. Taylor, “Improved regularization of convolutional neural networks with cutout,”
2017
Earlier work this paper cites.
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Cited alongside, same era.
L. Schott, J. Rauber, M. Bethge, and W. Brendel, “Towards the first adversarially robust neural network model on mnist,” in
2018
Cited alongside, same era.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing Robust Adversarial Examples,” in
2018
Cited alongside, same era.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards Deep Learning Models Resistant to Adversarial Attacks,” in
2018
Cited alongside, same era.
A. Galloway, G. W. Taylor, and M. Moussa, “Attacking binarized neural networks,” in
2018
Cited alongside, same era.
Y. Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang, “Unlabeled data improves adversarial robustness,”
2019
Later among the works it cites.
Y. Kaya, S. Hong, and T. Dumitras, “Shallow-deep networks: Understanding and mitigating network overthinking,” in
2019
Later among the works it cites.
J. Frankle and M. Carbin, “The lottery ticket hypothesis: Finding sparse, trainable neural networks,” in
2019
Later among the works it cites.
Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell, “Rethinking the value of network pruning,” in
2019
Later among the works it cites.
P. Maini, E. Wong, and J. Z. Kolter, “Adversarial robustness against the union of multiple perturbation models,” in
2020
Later among the works it cites.
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2018
Cited alongside, same era.
W. Xu, D. Evans, and Y. Qi, “Feature squeezing: Detecting adversarial examples in deep neural networks,” in
2018
Cited alongside, same era.
L. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in
2018
Cited alongside, same era.
L. Wang, G. W. Ding, R. Huang, Y. Cao, and Y. C. Lui, “Adversarial robustness of pruned neural networks,”
2018
Cited alongside, same era.
Y. Guo, C. Zhang, C. Zhang, and Y. Chen, “Sparse dnns with improved adversarial robustness,”
2018
Cited alongside, same era.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in
2018
Cited alongside, same era.
H. Zhang, M. Cissé, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in
2018
Cited alongside, same era.
H. Salman, A. Ilyas, L. Engstrom, A. Kapoor, and A. Madry, “Do adversarially robust imagenet models transfer better?” in
2020
Later among the works it cites.
C. Xie, M. Tan, B. Gong, J. Wang, A. L. Yuille, and Q. V. Le, “Adversarial examples improve image recognition,” in
2020
Later among the works it cites.
M. Andriushchenko and N. Flammarion, “Understanding and improving fast adversarial training,” in
2020
Later among the works it cites.
L. Rice, E. Wong, and J. Z. Kolter, “Overfitting in adversarially robust deep learning,” in
2020
Later among the works it cites.
P. Stock, A. Joulin, R. Gribonval, B. Graham, and H. Jégou, “And the bit goes down: Revisiting the quantization of neural networks,” in
2020
Later among the works it cites.
J. T. C. Min and M. Motani, “Dropnet: reducing neural network complexity via iterative pruning,” in
2020
Later among the works it cites.
M. Lin, R. Ji, Y. Wang, Y. Zhang, B. Zhang, Y. Tian, and L. Shao, “Hrank: Filter pruning using high-rank feature map,” in
2020
Later among the works it cites.
J.-H. Luo and J. Wu, “Neural network pruning with residual-connections and limited-data,” in
2020
Later among the works it cites.
A. Jordao, F. Yamada, and W. R. Schwartz, “Deep network compression based on partial least squares,”
2020
Later among the works it cites.
V. Sehwag, S. Wang, P. Mittal, and S. Jana, “Hydra: Pruning adversarially robust neural networks,”
2020
Later among the works it cites.
T. Hu, T. Chen, H. Wang, and Z. Wang, “Triple wins: Boosting accuracy, robustness and efficiency together by enabling input-adaptive inference,” in
2020
Later among the works it cites.
T. Pang, X. Yang, Y. Dong, H. Su, and J. Zhu, “Bag of Tricks for Adversarial Training,” in
2021
Later among the works it cites.
M. Gorsline, J. Smith, and C. Merkel, “On the adversarial robustness of quantized neural networks,” in
2021
Later among the works it cites.
A. Jordao and H. Pedrini, “On the effect of pruning on adversarial robustness,” in
2021
Later among the works it cites.
S. Varghese, C. Hümmer, A. Bär, F. Hüger, and T. Fingscheidt, “Joint optimization for dnn model compression and corruption robustness,”
2022
Later among the works it cites.
T. Stauner, F. Blank, M. Fürst, J. Günther, K. Hagn, P. Heidenreich, M. Huber, B. Knerr, T. Schulik, and K. Leiß, “Synpeds: A synthetic dataset for pedestrian detection in urban traffic scenes,” in
2022
Later among the works it cites.
N. Liao, S. Wang, L. Xiang, N. Ye, S. Shao, and P. Chu, “Achieving adversarial robustness via sparsity,”
2022
Later among the works it cites.