Fetching the paper…
Reading the bibliography…
CNNs are poised to become integral parts of many critical systems.
Receptive fields of single neurones in the cat’s striate cortex
D. H. Hubel and T. N. Wiesel · 1959
Earlier work this paper cites.
Mathematical description of the responses of simple cortical cells
S. Marc̆elja · 1980
Earlier work this paper cites.
Relations between the statistics of natural images and the response properties of cortical cells
D. J. Field · 1987
Earlier work this paper cites.
Image coding using wavelet transform
M. Antonini, M. Barlaud, P. Mathieu, and I. Daubechies · 1992
Earlier work this paper cites.
Adapting to unknown smoothness via wavelet shrinkage
D. L. Donoho and I. Johnstone · 1992
Earlier work this paper cites.
Ideal spatial adaptation by wavelet shrinkage
D. L. Donoho and I. Johnstone · 1994
Earlier work this paper cites.
Global training of document processing systems using graph transformer networks
L. Bottou, Y. Bengio, and Y. LeCun · 1997
Earlier work this paper cites.
Bayesian denoising of visual images in the wavelet domain
E. P. Simoncelli · 1999
Earlier work this paper cites.
Adaptive wavelet thresholding for image denoising and compression
S. G. Chang, B. Yu, and M. Vetterli · 2000
Earlier work this paper cites.
The jpeg - 2000 still image compression standard � ( last revised : June 30 , 2001 )
M. D. Adams · 2001
Earlier work this paper cites.
Image denoising using wavelets
R. Rangarajan, R. Venkataramanan, and S. Shah · 2002
Earlier work this paper cites.
Spatiotemporal elements of macaque v1 receptive fields
N. C. Rust, O. Schwartz, J. A. Movshon, and E. P. Simoncelli · 2005
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F. fei Li · 2009
Earlier work this paper cites.
Rudin-osher-fatemi total variation denoising using split bregman
P. Getreuer · 2012
Earlier work this paper cites.
Noise reduction by wavelet thresholding
M. Jansen · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Cited alongside, same era.
Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks
X. Huang, C. Shen, X. Boix, and Q. Zhao · 2015
Cited alongside, same era.
Foveation-based mechanisms alleviate adversarial examples
Y. Luo, X. Boix, G. Roig, T. A. Poggio, and Q. Zhao · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. M. Nguyen, J. Yosinski, and J. Clune · 2015
Cited alongside, same era.
Is object localization for free? - weakly-supervised learning with convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2015
Cited alongside, same era.
Towards evaluating the robustness of neural networks
N. Carlini and D. A. Wagner · 2017
Later among the works it cites.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhyay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2017
Later among the works it cites.
Keeping the bad guys out: Protecting and vaccinating deep learning with JPEG compression
N. Das, M. Shanbhogue, S.-T. Chen, F. Hohman, L. Chen, M. E. Kounavis, and D. H. Chau · 2017
Later among the works it cites.
Dirty pixels: Optimizing image classification architectures for raw sensor data
S. Diamond, V. Sitzmann, S. P. Boyd, G. Wetzstein, and F. Heide · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Understanding neural networks through deep visualization
J. Yosinski, J. Clune, A. M. Nguyen, T. J. Fuchs, and H. Lipson · 2015
Cited alongside, same era.
A study of the effect of JPG compression on adversarial images
G. K. Dziugaite, Z. Ghahramani, and D. M. Roy · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. X. Song · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
Later among the works it cites.
Countering adversarial images using input transformations
C. Guo, M. Rana, M. Cissé, and L. van der Maaten · 2017
Later among the works it cites.
Detecting adversarial attacks on neural network policies with visual foresight
Y.-C. Lin, M.-Y. Liu, M. Sun, and J.-B. Huang · 2017
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
Later among the works it cites.
Magnet: A two-pronged defense against adversarial examples
D. Meng and H. Chen · 2017
Later among the works it cites.
cleverhans v2.0.0: an adversarial machine learning library
I. G. R. F. F. F. A. M. K. H. Y.-L. J. A. K. R. S. A. G. Y.-C. L. Nicolas Papernot, Nicholas Carlini · 2017
Later among the works it cites.
Semantic perceptual image compression using deep convolution networks
A. Prakash, N. Moran, S. Garber, A. DiLillo, and J. Storer · 2017
Later among the works it cites.
One pixel attack for fooling deep neural networks
J. Su, D. V. Vargas, and K. Sakurai · 2017
Later among the works it cites.
Ensemble adversarial training: Attacks and defenses
F. Tramèr, A. Kurakin, N. Papernot, D. Boneh, and P. D. McDaniel · 2017
Later among the works it cites.
The space of transferable adversarial examples
F. Tramèr, N. Papernot, I. J. Goodfellow, D. Boneh, and P. D. McDaniel · 2017
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, and Y. Qi · 2017
Later among the works it cites.
Mitigating adversarial effects through randomization
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille · 2018
Closest in time.