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Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape.
Adversarial Robustness as a Prior for Learned Representations
Engstrom, L., Ilyas, A., Santurkar, S., Tsipras, D., Tran, B., and Madry, A · 1906
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Assessing shape bias property of convolutional neural networks
Hosseini, H., Xiao, B., Jaiswal, M., and Poovendran, R · 1931
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Forest before trees: The precedence of global features in visual perception
Navon, D · 1977
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An empirical study of learning speed in back-propagation networks
Fahlman, S. E · 1988
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The importance of shape in early lexical learning
Landau, B., Smith, L. B., and Jones, S. S · 1988
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Pyramid-based texture analysis/synthesis
Heeger, D. J., and Bergen, J. R · 1995
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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A parametric texture model based on joint statistics of complex wavelet coefficients
Portilla, J., and Simoncelli, E. P · 2000
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Image quilting for texture synthesis and transfer
Efros, A. A., and Freeman, W. T · 2001
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Shape and the first hundred nouns
Gershkoff-Stowe, L., and Smith, L. B · 2004
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Deep, big, simple neural nets for handwritten digit recognition
Cireşan, D. C., Meier, U., Gambardella, L. M., and Schmidhuber, J · 2010
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Statsmodels: Econometric and statistical modeling with python
Seabold, S., and Perktold, J · 2010
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Deep learners benefit more from out-of-distribution examples
Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T., Chherawala, Y., Cisse, M., Côté, M., Erhan, D., Eustache, J., et al · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y · 2013
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Deep neural networks rival the representation of primate it cortex for core visual object recognition
Cadieu, C. F., Hong, H., Yamins, D. L., Pinto, N., Ardila, D., Solomon, E. A., Majaj, N. J., and DiCarlo, J. J · 2014
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Discriminative unsupervised feature learning with convolutional neural networks
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and Brox, T · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Deep supervised, but not unsupervised, models may explain it cortical representation
Khaligh-Razavi, S.-M., and Kriegeskorte, N · 2014
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Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J · 2014
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One weird trick for parallelizing convolutional neural networks
Krizhevsky, A · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., and Zisserman, A · 2014
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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
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., and DiCarlo, J. J · 2014
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Manitest: Are classifiers really invariant?
Fawzi, A., and Frossard, P · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., and Clune, J · 2015
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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
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On the performance of googlenet and alexnet applied to sketches
Ballester, P., and Araujo, R. M · 2016
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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
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Donahue, J., Krähenbühl, P., and Darrell, T · 2016
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Adversarially learned inference
Dumoulin, V., Belghazi, I., Poole, B., Mastropietro, O., Lamb, A., Arjovsky, M., and Courville, A · 2016
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Image style transfer using convolutional neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
Cited alongside, same era.
Deep neural networks as a computational model for human shape sensitivity
Kubilius, J., Bracci, S., and de Beeck, H. P. O · 2016
Cited alongside, same era.
Object recognition with and without objects
Zhu, Z., Xie, L., and Yuille, A. L · 2016
Cited alongside, same era.
Eigen-distortions of hierarchical representations
Berardino, A., Laparra, V., Ballé, J., and Simoncelli, E · 2017
Randaugment: Practical data augmentation with no separate search
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2019
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Large scale adversarial representation learning
Donahue, J., and Simonyan, K · 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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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
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Augmix: A simple data processing method to improve robustness and uncertainty
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Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T., and Taylor, G. W · 2017
Cited alongside, same era.
A study and comparison of human and deep learning recognition performance under visual distortions
Dodge, S., and Karam, L · 2017
Cited alongside, same era.
Cognitive psychology for deep neural networks: A shape bias case study
Ritter, S., Barrett, D. G., Santoro, A., and Botvinick, M. M · 2017
Cited alongside, same era.
The marginal value of adaptive gradient methods in machine learning
Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., and Recht, B · 2017
Cited alongside, same era.
Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A., and Weiss, Y · 2018
Cited alongside, same era.
Deep convolutional networks do not classify based on global object shape
Baker, N., Lu, H., Erlikhman, G., and Kellman, P. J · 2018
Cited alongside, same era.
Hendrycks, D., Mu, N., Cubuk, E. D., Zoph, B., Gilmer, J., and Lakshminarayanan, B · 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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Are perceptually-aligned gradients a general property of robust classifiers?
Kaur, S., Cohen, J., and Lipton, Z. C · 2019
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Revisiting self-supervised visual representation learning
Kolesnikov, A., Zhai, X., and Beyer, L · 2019
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Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Li, Y., Wei, C., and Ma, T · 2019
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Fast autoaugment
Lim, S., Kim, I., Kim, T., Kim, C., and Kim, S · 2019
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
Lopes, R. G., Yin, D., Poole, B., Gilmer, J., and Cubuk, E. D · 2019
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On implicit filter level sparsity in convolutional neural networks
Mehta, D., Kim, K. I., and Theobalt, C · 2019
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When does label smoothing help?
Müller, R., Kornblith, S., and Hinton, G · 2019
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Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences
Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., and Livingstone, M. S · 2019
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Adversarial training can hurt generalization
Raghunathan, A., Xie, S. M., Yang, F., Duchi, J. C., and Liang, P · 2019
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Stand-alone self-attention in vision models
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., and Shlens, J · 2019
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Computer vision with a single (robust) classifier
Santurkar, S., Tsipras, D., Tran, B., Ilyas, A., Engstrom, L., and Madry, A · 2019
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Adversarial training and robustness for multiple perturbations
Tramèr, F., and Boneh, D · 2019
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Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
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A possible reason for why data-driven beats theory-driven computer vision
Tsotsos, J. K., Kotseruba, I., Andreopoulos, A., and Wu, Y · 2019
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Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
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Adversarial examples improve image recognition
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A., and Le, Q. V · 2019
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Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. v. d., Yuille, A. L., and He, K · 2019
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A fourier perspective on model robustness in computer vision
Yin, D., Lopes, R. G., Shlens, J., Cubuk, E. D., and Gilmer, J · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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The visual task adaptation benchmark
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., et al · 2019
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Humans can decipher adversarial images
Zhou, Z., and Firestone, C · 2019
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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Increasing the robustness of dnns against image corruptions by playing the game of noise
Rusak, E., Schott, L., Zimmermann, R., Bitterwolf, J., Bringmann, O., Bethge, M., and Brendel, W · 2020
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When robustness doesn’t promote robustness: Synthetic vs. natural distribution shifts on imagenet, 2020
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
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