Robustness of Deep Convolutional Neural Networks for Image Recognition
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[1] Nitish Srivastava,et al. Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.
[2] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[3] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[4] Ming Yang,et al. DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[5] Jürgen Schmidhuber,et al. Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.
[6] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[7] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[8] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[9] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[10] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[11] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..