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Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
Kunihiko Fukushima and Sei Miyake · 1982
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Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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Shiftable multiscale transforms
Eero P Simoncelli, William T Freeman, Edward H Adelson, and David J Heeger · 1992
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Scene summarization for online image collections
Ian Simon, Noah Snavely, and Steven M Seitz · 2007
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Computing iconic summaries of general visual concepts
Rahul Raguram and Svetlana Lazebnik · 2008
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Finding iconic images
Tamara L Berg and Alexander C Berg · 2009
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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Discovering favorite views of popular places with iconoid shift
Tobias Weyand and Bastian Leibe · 2011
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Learning about canonical views from internet image collections
Elad Mezuman and Yair Weiss · 2012
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Rotation, scaling and deformation invariant scattering for texture discrimination
Laurent Sifre and Stéphane Mallat · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2014
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Transformation properties of learned visual representations
Taco S Cohen and Max Welling · 2014
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Deep symmetry networks
Robert Gens and Pedro M Domingos · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Scale-invariant convolutional neural networks
Yichong Xu, Tianjun Xiao, Jiaxing Zhang, Kuiyuan Yang, and Zheng Zhang · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Rifd-cnn: Rotation-invariant and fisher discriminative convolutional neural networks for object detection
Gong Cheng, Peicheng Zhou, and Junwei Han · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Exploiting cyclic symmetry in convolutional neural networks
Sander Dieleman, Jeffrey De Fauw, and Koray Kavukcuoglu · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Carlos Esteves, Christine Allen-Blanchette, Xiaowei Zhou, and Kostas Daniilidis · 2017
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Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba · 2014
Cited alongside, same era.
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Sander Dieleman, Kyle W Willett, and Joni Dambre · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2015
Cited alongside, same era.
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.
Multi-scale context aggregation by dilated convolutions
Fisher Yu and Vladlen Koltun · 2015
Cited alongside, same era.
Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images
Gong Cheng, Peicheng Zhou, and Junwei Han
Cited in the paper.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Later among the works it cites.
Harmonic networks: Deep translation and rotation equivariance
Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow · 2017
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Dilated residual networks
Fisher Yu, Vladlen Koltun, and Thomas Funkhouser · 2017
Later among the works it cites.
Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay and Yair Weiss · 2018
Closest in time.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Closest in time.
Making convolutional networks shift-invariant again
Richard Zhang · 2019
Closest in time.