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Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes.
The importance of shape in early lexical learning
Barbara Landau, Linda B Smith, and Susan S Jones · 1988
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WordNet: a lexical database for English
George A Miller · 1995
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Deep neural networks rival the representation of primate IT cortex for core visual object recognition
Charles F Cadieu, H Hong, D L K Yamins, N Pinto, D Ardila, E A Solomon, N J Majaj, and J J DiCarlo · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Texture synthesis using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep neural networks: A new framework for modeling biological vision and brain information processing
N. Kriegeskorte · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
On the performance of GoogLeNet and AlexNet applied to sketches
Pedro Ballester and Ricardo Matsumura de Araújo · 2016
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Image style transfer using convolutional neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2016
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Deep neural networks as a computational model for human shape sensitivity
Jonas Kubilius, Stefania Bracci, and Hans P Op de Beeck · 2016
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A study and comparison of human and deep learning recognition performance under visual distortions
Samuel Dodge and Lina Karam · 2017
Cited alongside, same era.
Cognitive psychology for deep neural networks: A shape bias case study
Samuel Ritter, David GT Barrett, Adam Santoro, and Matt M Botvinick · 2017
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A parametric texture model based on deep convolutional features closely matches texture appearance for humans
Thomas SA Wallis, Christina M Funke, Alexander S Ecker, Leon A Gatys, Felix A Wichmann, and Matthias Bethge · 2017
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Sketch-a-net: A deep neural network that beats humans
Qian Yu, Yongxin Yang, Feng Liu, Yi-Zhe Song, Tao Xiang, and Timothy M Hospedales · 2017
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos M. Medina Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Improving shape deformation in unsupervised image-to-image translation
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Miguel P Eckstein, Kathryn Koehler, Lauren E Welbourne, and Emre Akbas · 2017
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Synthesising dynamic textures using convolutional neural networks
Christina M Funke, Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2017
Cited alongside, same era.
Texture and art with deep neural networks
Leon A Gatys, Alexander S Ecker, and Matthias Bethge · 2017
Cited alongside, same era.
Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos, David HJ Janssen, Heiko H Schütt, Jonas Rauber, Matthias Bethge, and Felix A Wichmann · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
Xun Huang and Serge J Belongie · 2017
Cited alongside, same era.
OpenImages: A public dataset for large-scale multi-label and multi-class image classification
Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch, 2017
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Aaron Gokaslan, Vivek Ramanujan, Daniel Ritchie, Kwang In Kim, and James Tompkin · 2018
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The unreasonable effectiveness of texture transfer for single image super-resolution
Muhammad W Gondal, Bernhard Schölkopf, and Michael Hirsch · 2018
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Assessing shape bias property of Convolutional Neural Networks
Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, and Radha Poovendran · 2018
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Correspondence of deep neural networks and the brain for visual textures
Md Nasir Uddin Laskar, Luis G Sanchez Giraldo, and Odelia Schwartz · 2018
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The role of textural statistics vs. outer contours in deep CNN and neural responses to objects
Bria Long and Talia Konkle · 2018
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Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, and Matthias Bethge · 2018
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Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet
Wieland Brendel and Matthias Bethge · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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