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Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Bruno A Olshausen and David J Field · 1996
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems
Amir Beck and Marc Teboulle · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
D. Erhan, Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2009
Earlier work this paper cites.
Deconvolutional networks
Matthew D Zeiler, Dilip Krishnan, Graham W Taylor, and Rob Fergus · 2010
Earlier work this paper cites.
Learning fast approximations of sparse coding
Karol Gregor and Yann LeCun · 2010
Earlier work this paper cites.
Task-driven dictionary learning
Julien Mairal, Francis Bach, and Jean Ponce · 2011
Earlier work this paper cites.
Exact recovery of sparsely-used dictionaries
Daniel A Spielman, Huan Wang, and John Wright · 2012
Earlier work this paper cites.
Sparse modeling for image and vision processing
Julien Mairal, Francis Bach, and Jean Ponce · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
Deep networks for image super-resolution with sparse prior
Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang · 2015
Earlier work this paper cites.
Efficient algorithms for convolutional sparse representations
Brendt Wohlberg · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 2015
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Earlier work this paper cites.
Complete dictionary recovery over the sphere i: Overview and the geometric picture
Ju Sun, Qing Qu, and John Wright · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
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Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
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Inverting visual representations with convolutional networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Convolutional neural networks analyzed via convolutional sparse coding
Vardan Papyan, Yaniv Romano, and Michael Elad · 2017
Cited alongside, same era.
Optnet: Differentiable optimization as a layer in neural networks
Brandon Amos and J Zico Kolter · 2017
Cited alongside, same era.
Differentiable learning of submodular models
Josip Djolonga and Andreas Krause · 2017
Cited alongside, same era.
Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
Laurent El Ghaoui, Fangda Gu, Bertrand Travacca, and Armin Askari · 2019
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Deep equilibrium models
Shaojie Bai, J Zico Kolter, and Vladlen Koltun · 2019
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Differentiable optimization-based modeling for machine learning
Brandon Amos · 2019
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Meyer Scetbon, Michael Elad, and Peyman Milanfar · 2019
Later among the works it cites.
A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D Cubuk, and Justin Gilmer · 2019
Later among the works it cites.
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Working locally thinking globally: Theoretical guarantees for convolutional sparse coding
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A mathematical introduction to compressive sensing
Simon Foucart and Holger Rauhut · 2017
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Multilayer convolutional sparse modeling: Pursuit and dictionary learning
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Supervised deep sparse coding networks
Xiaoxia Sun, Nasser M Nasrabadi, and Trac D Tran · 2018
Cited alongside, same era.
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Complete Dictionary Learning via ℓ 4 \ell_{4} -Norm Maximization over the Orthogonal Group
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Multiscale deep equilibrium models
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Implicit feature pyramid network for object detection
Tiancai Wang, Xiangyu Zhang, and Jian Sun · 2020
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Fully trainable and interpretable non-local sparse models for image restoration
Bruno Lecouat, Jean Ponce, and Julien Mairal · 2020
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A flexible framework for designing trainable priors with adaptive smoothing and game encoding
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Interpreting u-nets via task-driven multiscale dictionary learning
Tianlin Liu, Anadi Chaman, David Belius, and Ivan Dokmanić · 2020
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Adversarial noise attacks of deep learning architectures: Stability analysis via sparse-modeled signals
Yaniv Romano, Aviad Aberdam, Jeremias Sulam, and Michael Elad · 2020
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Adversarial robustness of supervised sparse coding
Jeremias Sulam, Ramchandran Muthukumar, and Raman Arora · 2020
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Deep isometric learning for visual recognition
Haozhi Qi, Chong You, Xiaolong Wang, Yi Ma, and Jitendra Malik · 2020
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Towards understanding residual and dilated dense neural networks via convolutional sparse coding
Zhiyang Zhang and Shihua Zhang · 2021
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Architectural adversarial robustness: The case for deep pursuit
George Cazenavette, Calvin Murdock, and Simon Lucey · 2021
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Convolutional normalization: Improving deep convolutional network robustness and training
Sheng Liu, Xiao Li, Yuexiang Zhai, Chong You, Zhihui Zhu, Carlos Fernandez-Granda, and Qing Qu · 2021
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High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications
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