Fetching the paper…
Reading the bibliography…
Attention plays a critical role in human visual experience.
Improving generalization performance using double backpropagation
H. Drucker and Y LeCun · 1992
Earlier work this paper cites.
The dynamic representation of scenes
Ronald A. Rensink · 2000
Earlier work this paper cites.
Model compression
Cristian Bucila, Rich Caruana, and Alexandru Niculescu-Mizil · 2006
Earlier work this paper cites.
Recognizing indoor scenes
A. Quattoni and A. Torralba · 2009
Earlier work this paper cites.
Learning to combine foveal glimpses with a third-order boltzmann machine
Hugo Larochelle and Geoffrey E. Hinton · 2010
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Learning where to attend with deep architectures for image tracking
Misha Denil, Loris Bazzani, Hugo Larochelle, and Nando de Freitas · 2012
Earlier work this paper cites.
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Do deep nets really need to be deep?
Jimmy Ba Lei and Rich Caruana · 2014
Cited alongside, same era.
Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, and koray kavukcuoglu · 2014
Cited alongside, same era.
FitNets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Cited alongside, same era.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew Zeiler and Rob Fergus · 2014
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Later among the works it cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
Later among the works it cites.
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
Later among the works it cites.
Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alexander J. Smola · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Distilling the knowledge in a neural networks
Geoffrey E. Hinton, Oriol Vinyals, and Jeff Dean · 2015
Cited alongside, same era.
Is object localization for free? – weakly-supervised learning with convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2015
Cited alongside, same era.
Deep face recognition
O. M. Parkhi, A. Vedaldi, and A. Zisserman · 2015
Cited alongside, same era.
Taco S. Cohen and Max Welling · 2016
Closest in time.
Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2016
Closest in time.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Closest in time.
Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
Closest in time.