Fetching the paper…

Reading the bibliography…

2015

A Unified Gradient Regularization Family for Adversarial Examples

Lyu, Chunchuan, Huang, Kaizhu, Liang, Hai-Ning

Original
PDF
Post on X

Understand

Adversarial examples are augmented data points generated by imperceptible perturbation of input samples.

They have recently drawn much attention with the machine learning and data mining community.
Being difficult to distinguish from real examples, such adversarial examples could change the prediction of many of the best learning models including the state-of-the-art deep learning models.
Recent attempts have been made to build robust models that take into account adversarial examples.

A Unified Gradient Regularization Family for Adversarial Examples · Around

Built on

C. M. Bishop, “Training with noise is equivalent to tikhonov regularization,” Neural Computation , vol. 7, pp. 108–116, 1994
1994
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
N. N. Schraudolph, “Fast curvature matrix-vector products for second-order gradient descent,” Neural Computation , vol. 14, p. 2002, 2002
Original
2002
Earlier work this paper cites.
D. Lowd and C. Meek, “Adversarial learning,” in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining , ser. KDD ’05. New York, NY, USA: ACM, 2005, pp. 641–647. [Online]. Available: http://doi.acm.org/10.1145/1081870.1081950
2005
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” Computer Science Department, University of Toronto, Tech. Rep , vol. 1, no. 4, p. 7, 2009
2009
Earlier work this paper cites.
J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio, “Theano: a CPU and GPU math expression compiler,” in Proceedings of the Python for Scientific Computing Conference (SciPy) , Jun. 2010, oral Presentation
2010
Earlier work this paper cites.
P. Sermanet, S. Chintala, and Y. LeCun, “Convolutional neural networks applied to house numbers digit classification,” in Pattern Recognition (ICPR), 2012 21st International Conference on . IEEE, 2012, pp. 3288–3291
2012
Earlier work this paper cites.
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Improving neural networks by preventing co-adaptation of feature detectors,” CoRR , vol. abs/1207.0580, 2012. [Online]. Available: http://arxiv.org/abs/1207.0580
Original
2012
Earlier work this paper cites.

Similar

R. B. Palm, “Prediction as a candidate for learning deep hierarchical models of data,” 2012
2012
Cited alongside, same era.
F. Bastien, P. Lamblin, R. Pascanu, J. Bergstra, I. J. Goodfellow, A. Bergeron, N. Bouchard, and Y. Bengio, “Theano: new features and speed improvements,” Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop, 2012
2012
Cited alongside, same era.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” CoRR , vol. abs/1312.6199, 2013. [Online]. Available: http://arxiv.org/abs/1312.6199
Original
2013
Cited alongside, same era.
I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, “Maxout Networks,” Sep. 2013. [Online]. Available: http://arxiv.org/abs/1302.4389
Original
2013
Cited alongside, same era.
I. J. Goodfellow, D. Warde-Farley, P. Lamblin, V. Dumoulin, M. Mirza, R. Pascanu, J. Bergstra, F. Bastien, and Y. Bengio, “Pylearn2: a machine learning research library,” arXiv preprint arXiv:1308.4214 , 2013. [Online]. Available: http://arxiv.org/abs/1308.4214

Then

S. Gu and L. Rigazio, “Towards deep neural network architectures robust to adversarial examples,” arXiv preprint arXiv:1412.5068 , 2014
Original
2014
Later among the works it cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Computer Vision–ECCV 2014 . Springer, 2014, pp. 818–833
2014
Later among the works it cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. , vol. 15, no. 1, pp. 1929–1958, Jan. 2014. [Online]. Available: http://dl.acm.org/citation.cfm?id=2627435.2670313
2014
Later among the works it cites.
B. Poole, J. Sohl-Dickstein, and S. Ganguli, “Analyzing noise in autoencoders and deep networks,” CoRR , vol. abs/1406.1831, 2014. [Online]. Available: http://arxiv.org/abs/1406.1831
Original
2014
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2013

Cited alongside, same era.

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014

2014

Cited alongside, same era.

A. Nguyen, J. Yosinski, and J. Clune, “Deep neural networks are easily fooled: High confidence predictions for unrecognizable images,” arXiv preprint arXiv:1412.1897 , 2014

2014

Cited alongside, same era.

F. Wang, W. Liu, and S. Chawla, “On sparse feature attacks in adversarial learning,” in Data Mining (ICDM), 2014 IEEE International Conference on , Dec 2014, pp. 1013–1018

2014

Cited alongside, same era.

J. Mairal, P. Koniusz, Z. Harchaoui, and C. Schmid, “Convolutional kernel networks,” in Advances in Neural Information Processing Systems 27 , Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2627–2635. [Online]. Available: http://papers.nips.cc/paper/5348-convolutional-kernel-networks.pdf

2014

Later among the works it cites.

A. Fawzi, O. Fawzi, and P. Frossard, “Analysis of classifiers’ robustness to adversarial perturbations,” CoRR , vol. abs/1502.02590, 2015. [Online]. Available: http://arxiv.org/abs/1502.02590

2015

Closest in time.

A. Dosovitskiy and T. Brox, “Inverting convolutional networks with convolutional networks,” CoRR , vol. abs/1506.02753, 2015. [Online]. Available: http://arxiv.org/abs/1506.02753

2015

Closest in time.