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Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions.
Possible principles underlying the transformation of sensory messages
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Noise injection for inputs relevance determination
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Natural image statistics and neural representation
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Statistics of natural image categories
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Imagenet: A large-scale hierarchical image database
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JPEG Interchange Format (JFIF)
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Learning multiple layers of features from tiny images
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Lessons learned from manually classifying cifar-10
Karpathy, A · 2011
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Imagenet classification with deep convolutional neural networks
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Microsoft coco: Common objects in context
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Analyzing noise in autoencoders and deep networks, 2014
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Very deep convolutional networks for large-scale image recognition
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Going deeper with convolutions
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Intriguing properties of adversarial examples
Cubuk, E. D., Zoph, B., Schoenholz, S. S., and Le, Q. V · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S. and Karam, L · 2017
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Gastaldi, X · 2017
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Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 2017
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Benchmarking neural network robustness to common corruptions and surface variations
Hendrycks, D. and Dietterich, T. G · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Chen, D., Lee, H., Ngiam, J., Le, Q. V., and Chen, Z · 2018
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Excessive invariance causes adversarial vulnerability
Jacobsen, J.-H., Behrmann, J., Zemel, R., and Bethge, M · 2018
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Darts: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2018
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Hu, J., Shen, L., and Sun, G · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A · 2017
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
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Random erasing data augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y · 2017
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
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Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2018
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Rosenfeld, A., Zemel, R., and Tsotsos, J. K · 2018
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
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Surprising effectiveness of few-image unsupervised feature learning, 2019
Asano, Y. M., Rupprecht, C., and Vedaldi, A · 2019
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M · 2019
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Adversarial examples are a natural consequence of test error in noise
Ford, N., Gilmer, J., Carlini, N., and Cubuk, D · 2019
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Using videos to evaluate image model robustness
Gu, K., Yang, B., Ngiam, J., Le, Q., and Shlens, J · 2019
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Using pre-training can improve model robustness and uncertainty
Hendrycks, D., Lee, K., and Mazeika, M · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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Do imagenet classifiers generalize to imagenet?
Recht, B., Roelofs, R., Schmidt, L., and Shankar, V · 2019
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Learning robust representations by projecting superficial statistics out
Wang, H., He, Z., Lipton, Z. C., and Xing, E. P · 2019
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Unsupervised data augmentation, 2019
Xie, Q., Dai, Z., Hovy, E., Luong, M.-T., and Le, Q. V · 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
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