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This article presents the prediction difference analysis method for visualizing the response of a deep neural network to a specific input.
Classifying brain states and determining the discriminating activation patterns: Support vector machine on functional mri data
Janaina Mourao-Miranda, Arun LW Bokde, Christine Born, Harald Hampel, and Martin Stetter · 2005
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Non-linear optimisation. fmrib technical report tr07ja1
Jesper LR Andersson, Mark Jenkinson, and Stephen Smith · 2007
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Support vector machine learning-based fmri data group analysis
Ze Wang, Anna R Childress, Jiongjiong Wang, and John A Detre · 2007
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Automatic classification of mr scans in alzheimer’s disease
Stefan Klöppel, Cynthia M Stonnington, Carlton Chu, Bogdan Draganski, Rachael I Scahill, Jonathan D Rohrer, Nick C Fox, Clifford R Jack, John Ashburner, and Richard SJ Frackowiak · 2008
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Explaining classifications for individual instances
Marko Robnik-Šikonja and Igor Kononenko · 2008
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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Describing the brain in autism in five dimensions—magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach
Christine Ecker, Andre Marquand, Janaina Mourão-Miranda, Patrick Johnston, Eileen M Daly, Michael J Brammer, Stefanos Maltezos, Clodagh M Murphy, Dene Robertson, Steven C Williams, et al · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification
Bilwaj Gaonkar and Christos Davatzikos · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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On the interpretation of weight vectors of linear models in multivariate neuroimaging
Stefan Haufe, Frank Meinecke, Kai Görgen, Sven Dähne, John-Dylan Haynes, Benjamin Blankertz, and Felix Bießmann · 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
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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A data-centric neuroscience gateway: design, implementation, and experiences
Shayan Shahand, Ammar Benabdelkader, Mohammad Mahdi Jaghoori, Mostapha al Mourabit, Jordi Huguet, Matthan WA Caan, Antoine HC Kampen, and Sílvia D Olabarriaga · 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
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Understanding neural networks through deep visualization
Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson · 2015
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Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Cited alongside, same era.
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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