Towards deep neural network architectures robust to adversarial examples

3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings

Volumn , Issue , 2015, Pages

  Gu, Shixiang ^a   Rigazio, Luca ^a  

^a Panasonic Corporation of North America   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EQUATIONS; MULTILAYER NEURAL NETWORKS; NETWORK ARCHITECTURE;

NETWORK ROBUSTNESS; NETWORK TOPOLOGY; PERFORMANCE PENALTIES; PRE-PROCESSING; SMALL PERTURBATIONS; STATE OF THE ART; TRAINING PROCEDURES; TRAINING STRATEGY;

DEEP NEURAL NETWORKS;

EID: 85083953152     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Alain, Guillaume and Bengio, Yoshua. What regularized auto-encoders learn from the data generating distribution. arXiv preprint arXiv:1211.4246, 2012.
2. Bengio, Yoshua. Learning deep architectures for ai. Foundations and trendsR in Machine Learning, 2(1):1–127, 2009.
3. Chen, Minmin, Weinberger, Kilian, Sha, Fei, and Bengio, Yoshua. Marginalized denoising autoencoders for nonlinear representations. JMLR, 32(1):1476–1484, 2014.
4. Dahl, George E, Yu, Dong, Deng, Li, and Acero, Alex. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1):30–42, 2012.
5. Douglas, Rodney J, Koch, Christof, Mahowald, Misha, Martin, KA, and Suarez, Humbert H. Recurrent excitation in neocortical circuits. Science, 269(5226):981–985, 1995.
6. Duvenand, David, Rippel, Oren, Adams, Ryan P., and Ghahramani, Zoubin. Avoiding pathologies in very deep networks. arXiv preprint arXiv:1402.5836, 2014.
7. Felleman, Daniel J and Van Essen, David C. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex, 1(1):1–47, 1991.
8. Hinton, Geoffrey E, Krizhevsky, Alex, and Wang, Sida D. Transforming auto-encoders. In Artificial Neural Networks and Machine Learning–ICANN 2011, pp. 44–51. Springer, 2011.
9. Krizhevsky, Alex and Hinton, Geoffrey. Learning multiple layers of features from tiny images. Computer Science Department, University of Toronto, Tech. Rep, 2009.
10. Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E. Imagenet classification with deep convolutional neural networks. In NIPS, volume 1, pp. 4, 2012.
11. LeCun, Yann and Cortes, Corinna. The mnist database of handwritten digits, 1998.
12. Mnih, Volodymyr. Cudamat: a cuda-based matrix class for python. Department of Computer Science, University of Toronto, Tech. Rep. UTML TR, 4, 2009.
13. Mnih, Volodymyr, Heess, Nicolas, Graves, Alex, and Kavukcuoglu, Koray. Recurrent models of visual attention. CoRR, abs/1406.6247, 2014. URL http://arxiv.org/abs/1406.6247.
14. Rifai, Salah, Mesnil, Grégoire, Vincent, Pascal, Muller, Xavier, Bengio, Yoshua, Dauphin, Yann, and Glorot, Xavier. Higher order contractive auto-encoder. In Machine Learning and Knowledge Discovery in Databases, pp. 645–660. Springer, 2011a.
15. Rifai, Salah, Vincent, Pascal, Muller, Xavier, Glorot, Xavier, and Bengio, Yoshua. Contractive autoencoders: Explicit invariance during feature extraction. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 833–840, 2011b.
16. Stollenga, Marijn, Masci, Jonathan, Gomez, Faustino, and Schmidhuber, Juergen. Deep networks with internal selective attention through feedback connections. arXiv preprint arXiv:1407.3068, 2014.
17. Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. arXiv preprint arXiv:1409.4842, 2014a.
18. Szegedy, Christian, Zaremba, Wojciech, Sutskever, Ilya, Bruna, Joan, Erhan, Dumitru, Goodfellow, Ian, and Fergus, Rob. Intriguing properties of neural networks. ICLR, 2014b.
19. Taigman, Yaniv, Yang, Ming, Ranzato, Marc’Aurelio, and Wolf, Lior. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708, 2013.
20. Tang, Yichuan. Deep learning using support vector machines. arXiv preprint arXiv:1306.0239, 2013.
21. Vinyals, Oriol, Jia, Yangqing, Deng, Li, and Darrell, Trevor. Learning with recursive perceptual representations. In Advances in Neural Information Processing Systems, pp. 2825–2833, 2012.
22. Zhang, Ning, Paluri, Manohar, Ranzato, Marc’Aurelio, Darrell, Trevor, and Bourdev, Lubomir. Panda: Pose aligned networks for deep attribute modeling. arXiv preprint arXiv:1311.5591, 2013.