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In this paper, we study the sensitivity of CNN outputs with respect to image transformations and noise in the area of fine-grained recognition.
Sensitivity analysis of multilayer perceptron with differentiable activation functions
Jin Young Choi and Chong-Ho Choi · 1992
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Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions
Sherif Hashem · 1992
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Measuring invariances in deep networks
Ian Goodfellow, Honglak Lee, Quoc V Le, Andrew Saxe, and Andrew Y Ng · 2009
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jagannath Malik · 2014
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Towards deep neural network architectures robust to adversarial examples
Shixiang Gu and Luca Rigazio · 2014
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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross B Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sachin Shetty, Tommy Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2014
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Anh Nguyen, Jason Yosinski, and Jeff Clune · 2014
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Convolutional patch networks with spatial prior for road detection and urban scene understanding
Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, and Joachim Denzler · 2015
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Fundamental limits on adversarial robustness
Alhussein Fawzi, Omar Fawzi, and Pascal Frossard · 2015
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 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, et al · 2014
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Part detector discovery in deep convolutional neural networks
Marcel Simon, Erik Rodner, and Joachim Denzler · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Katen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Higher order contractive auto-encoder
Salah Rifai, Grégoire Mesnil, Pascal Vincent, Xavier Muller, Yoshua Bengio, Yann Dauphin, and Xavier Glorot
Cited in the paper.
Contractive auto-encoders: Explicit invariance during feature extraction
Salah Rifai, Pascal Vincent, Xavier Muller, Xavier Glorot, and Yoshua Bengio
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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
Cited in the paper.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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How well can a cnn marginalize simple nuisances it is designed for?
Nikolaos Karianakis, Jingming Dong, and Stefano Soatto · 2015
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Distributional smoothing by virtual adversarial examples
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii · 2015
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Neural activation constellations: Unsupervised part model discovery with convolutional networks
Marcel Simon and Erik Rodner · 2015
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