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Increasingly more similarities between human vision and convolutional neural networks (CNNs) have been revealed in the past few years.
Improved adversarial robustness by reducing open space risk via tent activations
Rozsa, A. and Boult, T. E. (2019) · 1908
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Random deep neural networks are biased towards simple functions
De Palma, G., Kiani, B., and Lloyd, S. (2019) · 1974
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Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., and Fatemi, E. (1992) · 1992
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014) · 2014
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015) · 2015
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Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., and Oliva, A. (2016) · 2016
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Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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A closer look at memorization in deep networks
Arpit, D., Jastrzebski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. (2017) · 2017
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A. (2017) · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2017) · 2017
Cited alongside, same era.
Coco-stuff: Thing and stuff classes in context
Caesar, H., Uijlings, J., and Ferrari, V. (2018) · 2018
Cited alongside, same era.
Generalisation in humans and deep neural networks
Geirhos, R., Temme, C. R., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A. (2018) · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2018) · 2018
Cited alongside, same era.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rajalingham, R., Issa, E. B., Bashivan, P., Kar, K., Schmidt, K., and DiCarlo, J. J. (2018) · 2018
Cited alongside, same era.
On the spectral bias of neural networks
Rahaman, N., Baratin, A., Arpit, D., Dräxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A. C. (2019) · 2019
Later among the works it cites.
Image synthesis with a single (robust) classifier
Santurkar, S., Ilyas, A., Tsipras, D., Engstrom, L., Tran, B., and Madry, A. (2019) · 2019
Later among the works it cites.
Deep learning: the good, the bad, and the ugly
Serre, T. (2019) · 2019
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A. (2019) · 2019
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Deep learning generalizes because the parameter-function map is biased towards simple functions
Valle-Perez, G., Camargo, C. Q., and Louis, A. A. (2019) · 2019
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A fourier perspective on model robustness in computer vision
Yin, D., Gontijo Lopes, R., Shlens, J., Cubuk, E. D., and Gilmer, J. (2019) · 2019
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M. (2019) · 2019
Cited alongside, same era.
Robustness (python library)
Engstrom, L., Ilyas, A., Santurkar, S., and Tsipras, D. (2019) · 2019
Cited alongside, same era.
Adversarial examples are a natural consequence of test error in noise
Ford, N., Gilmer, J., and Cubuk, E. D. (2019) · 2019
Cited alongside, same era.
Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W. (2019) · 2019
Cited alongside, same era.
Adversarial examples are a natural consequence of test error in noise
Gilmer, J., Ford, N., Carlini, N., and Cubuk, E. (2019) · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T. (2019) · 2019
Cited alongside, same era.
Understanding neural networks via feature visualization: A survey
Nguyen, A., Yosinski, J., and Clune, J. (2019) · 2019
Cited alongside, same era.
Later among the works it cites.
Interpreting adversarially trained convolutional neural networks
Zhang, T. and Zhu, Z. (2019) · 2019
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Sam: The sensitivity of attribution methods to hyperparameters
Bansal, N., Agarwal, C., and Nguyen, A. (2020) · 2020
Closest in time.
Adversarial robustness as a prior for learned representations
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Tran, B., and Madry, A. (2020) · 2020
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Imagemagick
ImageMagick (2020) · 2020
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Do adversarially robust imagenet models transfer better?
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., and Madry, A. (2020) · 2020
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Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A. L., and Le, Q. V. (2020) · 2020
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Intriguing properties of adversarial training at scale
Xie, C. and Yuille, A. (2020) · 2020
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