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In this paper we propose to augment a modern neural-network architecture with an attention model inspired by human perception.
Learning multi-attention convolutional neural network for fine-grained image recognition
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Object-based attention in the primary visual cortex of the macaque monkey
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Rectified linear units improve restricted Boltzmann machines
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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20 years of learning about vision: Questions answered, questions unanswered, and questions not yet asked
Bruno A Olshausen · 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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Multiple object recognition with visual attention
Jimmy Ba, Volodymyr Mnih, and Koray Kavukcuoglu · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Neural mechanisms of object-based attention
Daniel Baldauf and Robert Desimone · 2014
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Deep neural networks rival the representation of primate it cortex for core visual object recognition
Charles F. Cadieu, Ha Hong, Daniel L. K. Yamins, Nicolas Pinto, Diego Ardila, Ethan A. Solomon, Najib J. Majaj, and James J. DiCarlo · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom · 2015
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Foveation-based mechanisms alleviate adversarial examples
Yan Luo, Xavier Boix, Gemma Roig, Tomaso A. Poggio, and Qi Zhao · 2015
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The application of two-level attention models in deep convolutional neural network for fine-grained image classification
Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang · 2015
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Residual attention network for image classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Cram: Clued recurrent attention model
Minki Chung and Sungzoon Cho · 2018
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Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Universal adversarial perturbations
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2016
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Sheng-syun Shen and Hung-yi Lee · 2016
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Hierarchical attention networks for document classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy · 2016
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Enriched deep recurrent visual attention model for multiple object recognition
Artsiom Ablavatski, Shijian Lu, and Jianfei Cai · 2017
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Synthesizing robust adversarial examples
Anish Athalye and Ilya Sutskever · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Adversarial examples that fool both computer vision and time-limited humans
Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein · 2018
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Evaluating and understanding the robustness of adversarial logit pairing
Logan Engstrom, Andrew Ilyas, and Anish Athalye · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Adam R Kosiorek, Hyunjik Kim, Ingmar Posner, and Yee Whye Teh · 2018
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Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, and Alexander Ku · 2018
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Adversarial risk and the dangers of evaluating against weak attacks
Jonathan Uesato, Brendan O’Donoghue, Aaron van den Oord, and Pushmeet Kohli · 2018
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Attention, please! adversarial defense via attention rectification and preservation
Shangxi Wu, Jitao Sang, Kaiyuan Xu, Jiaming Zhang, Yanfeng Sun, Liping Jing, and Jian Yu · 2018
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L. Yuille, and Kaiming He · 2018
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Self-attention generative adversarial networks
Han Zhang, Ian J. Goodfellow, Dimitris N. Metaxas, and Augustus Odena · 2018
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An alternative surrogate loss for pgd-based adversarial testing, 2019
Sven Gowal, Jonathan Uesato, Chongli Qin, Po-Sen Huang, Timothy Mann, and Pushmeet Kohli · 2019
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Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2019
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Towards interpretable reinforcement learning using attention augmented agents
Alex Mott, Daniel Zoran, Mike Chrzanowski, Daan Wierstra, and Danilo J Rezende · 2019
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Adversarial robustness through local linearization
Chongli Qin, James Martens, Sven Gowal, Dilip Krishnan, Alhussein Fawzi, Soham De, Robert Stanforth, Pushmeet Kohli, et al · 2019
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Are labels required for improving adversarial robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang, Robert Stanforth, Alhussein Fawzi, and Pushmeet Kohli · 2019
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