Towards Robust Deep Neural Networks with BANG
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[1] Pascal Frossard,et al. Fundamental limits on adversarial robustness , 2015, ICML 2015.
[2] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[3] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[4] Terrance E. Boult,et al. Are facial attributes adversarially robust? , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[5] Qi Zhao,et al. Foveation-based Mechanisms Alleviate Adversarial Examples , 2015, ArXiv.
[6] P. Cochat,et al. Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.
[7] Seyed-Mohsen Moosavi-Dezfooli,et al. Robustness of classifiers: from adversarial to random noise , 2016, NIPS.
[8] Matthias Hein,et al. Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation , 2017, NIPS.
[9] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[10] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[11] Terrance E. Boult,et al. Adversarial Diversity and Hard Positive Generation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[12] Hanjiang Lai,et al. Simultaneous feature learning and hash coding with deep neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[16] Yvan Saeys,et al. Lower bounds on the robustness to adversarial perturbations , 2017, NIPS.
[17] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[18] Yang Song,et al. Improving the Robustness of Deep Neural Networks via Stability Training , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Harris Drucker,et al. Learning algorithms for classification: A comparison on handwritten digit recognition , 1995 .
[20] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Venkatesh Saligrama,et al. Efficient Training of Very Deep Neural Networks for Supervised Hashing , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Terrance E. Boult,et al. Assessing Threat of Adversarial Examples on Deep Neural Networks , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).
[23] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[24] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[26] Bin Yang,et al. Convolutional Channel Features , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[27] David A. Wagner,et al. Defensive Distillation is Not Robust to Adversarial Examples , 2016, ArXiv.
[28] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[29] Samy Bengio,et al. Adversarial Machine Learning at Scale , 2016, ICLR.
[30] Jen-Hao Hsiao,et al. Deep learning of binary hash codes for fast image retrieval , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[31] Xiaogang Wang,et al. DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).