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Convolutional Neural Networks (CNNs) are known to rely more on local texture rather than global shape when making decisions.
A mathematical theory of communication
Shannon, C. E · 1948
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Discrete cosine transform
Ahmed, N., Natarajan, T., and Rao, K. R · 1974
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Recognition-by-components: a theory of human image understanding
Biederman, I · 1987
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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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Texture feature extraction using gray level gradient based co-occurence matrices
Lam, S.-C · 1996
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A model of saliency-based visual attention for rapid scene analysis
Itti, L., Koch, C., and Niebur, E · 1998
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Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
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How specific is the shape bias?
Diesendruck, G. and Bloom, P · 2003
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Attentive object detection using an information theoretic saliency measure
Fritz, G., Seifert, C., Paletta, L., and Bischof, H · 2004
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An information maximization model of eye movements
Renninger, L. W., Coughlan, J. M., Verghese, P., and Malik, J · 2005
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Saliency based on information maximization
Bruce, N. and Tsotsos, J · 2006
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A kernel method for the two-sample-problem
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A. J · 2007
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Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
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Saliency detection: A spectral residual approach
Hou, X. and Zhang, L · 2007
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Perception of object shape and texture in human newborns: evidence from cross-modal transfer tasks
Sann, C. and Streri, A · 2007
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Saliency, attention, and visual search: An information theoretic approach
Bruce, N. D. and Tsotsos, J. K · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Schölkopf, B · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Learning in children: Progress in cognitive development research
Bisanz, J., Bisanz, G. L., and Kail, R · 2012
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How do humans sketch objects?
Eitz, M., Hays, J., and Alexa, M · 2012
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Undoing the damage of dataset bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., and Torralba, A · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Domain generalization via invariant feature representation
Muandet, K., Balduzzi, D., and Schölkopf, B · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Eye movements and vision
Yarbus, A. L · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
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Metareg: Towards domain generalization using meta-regularization
Balaji, Y., Sankaranarayanan, S., and Chellappa, R · 2018
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Domain generalization with domain-specific aggregation modules
D’Innocente, A. and Caputo, B · 2018
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Few-shot learning with metric-agnostic conditional embeddings
Hilliard, N., Phillips, L., Howland, S., Yankov, A., Corley, C. D., and Hodas, N. O · 2018
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Deep neural networks: a new framework for modeling biological vision and brain information processing
Kriegeskorte, N · 2015
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Deep learning
LeCun, Y., Bengio, Y., and Hinton, G · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 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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Domain separation networks
Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., and Erhan, D · 2016
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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Best sources forward: domain generalization through source-specific nets
Mancini, M., Bulò, S. R., Caputo, B., and Ricci, E · 2018
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Deep shape matching
Radenovic, F., Tolias, G., and Chum, O · 2018
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Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M · 2018
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Why do deep convolutional networks generalize so poorly to small image transformations?
Azulay, A. and Weiss, Y · 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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Domain generalization by solving jigsaw puzzles
Carlucci, F. M., D’Innocente, A., Bucci, S., Caputo, B., and Tommasi, T · 2019
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A closer look at few-shot classification
Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C. F., and Huang, J.-B · 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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Adversarial examples are a natural consequence of test error in noise
Gilmer, J., Ford, N., Carlini, N., and Cubuk, E · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Shape features improve general model robustness
Xiao, C., Sun, M., Qiu, H., Liu, H., Liu, M., and Li, B · 2019
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You only propagate once: Accelerating adversarial training via maximal principle
Zhang, D., Zhang, T., Lu, Y., Zhu, Z., and Dong, B · 2019
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Interpreting adversarially trained convolutional neural networks
Zhang, T. and Zhu, Z · 2019
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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A., and Soatto, S · 2020
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Are few-shot learning benchmarks too simple ?, 2020
Huang, G., Larochelle, H., and Lacoste-Julien, S · 2020
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