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Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations.
How to choose an activation function
H. N. Mhaskar and C. A. Micchelli · 1994
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
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Fast r-cnn
R. Girshick · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial machine learning at scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Large-margin softmax loss for convolutional neural networks
W. Liu, Y. Wen, Z. Yu, and M. Yang · 2016
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Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Improving face verification and person re-identification accuracy using hyperplane similarity
M. Jones and H. Kobori · 2017
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Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
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Deep hyperspherical learning
W. Liu, Y.-M. Zhang, X. Li, Z. Yu, B. Dai, T. Zhao, and L. Song · 2017
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Rethinking feature discrimination and polymerization for large-scale recognition
Y. Liu, H. Li, and X. Wang · 2017
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L2-constrained softmax loss for discriminative face verification
R. Ranjan, C. D. Castillo, and R. Chellappa · 2017
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S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
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cleverhans v2.0.0: an adversarial machine learning library
N. Papernot, N. Carlini, I. Goodfellow, R. Feinman, F. Faghri, A. Matyasko, K. Hambardzumyan, Y.-L. Juang, A. Kurakin, R. Sheatsley, et al · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
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Formal guarantees on the robustness of a classifier against adversarial manipulation
M. Hein and M. Andriushchenko · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten · 2017
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F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. McDaniel · 2017
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F. Wang, X. Xiang, J. Cheng, and A. L. Yuille · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Feature incay for representation regularization
Y. Yuan, K. Yang, and C. Zhang · 2017
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Additive margin softmax for face verification
F. Wang, W. Liu, H. Liu, and J. Cheng · 2018
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