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Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
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Efficient matching and indexing of graph models in content-based retrieval
Stefano Berretti, Alberto Del Bimbo, and Enrico Vicario · 2001
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Simrank: a measure of structural-context similarity
Glen Jeh and Jennifer Widom · 2002
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Rascal: Calculation of graph similarity using maximum common edge subgraphs
John W Raymond, Eleanor J Gardiner, and Peter Willett · 2002
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Dimensionality reduction for supervised learning with reproducing kernel hilbert spaces
Kenji Fukumizu, Francis R Bach, and Michael I Jordan · 2004
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Canonical correlation analysis: An overview with application to learning methods
David R Hardoon, Sandor Szedmak, and John Shawe-Taylor · 2004
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Graph indexing: a frequent structure-based approach
Xifeng Yan, Philip S Yu, and Jiawei Han · 2004
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Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Schölkopf · 2005
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Algorithms for learning kernels based on centered alignment
Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh · 2012
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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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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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The structure and dynamics of multilayer networks
Stefano Boccaletti, Ginestra Bianconi, Regino Criado, Charo I Del Genio, Jesús Gómez-Gardenes, Miguel Romance, Irene Sendina-Nadal, Zhen Wang, and Massimiliano Zanin · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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An exact graph edit distance algorithm for solving pattern recognition problems
Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau · 2015
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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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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Understanding deep image representations by inverting them
Aravindh Mahendran and Andrea Vedaldi · 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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Adversarial examples in the physical world, 2016
Alexey Kurakin, Ian Goodfellow, Samy Bengio, et al · 2016
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The normalization of occurrence and c o-occurrence matrices in bibliometrics using cosine similarities and o chiai coefficients
Qiuju Zhou and Loet Leydesdorff · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Deep neural networks as gaussian processes
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S Schoenholz, Jeffrey Pennington, and Jascha Sohl-Dickstein · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Backpropagating linearly improves transferability of adversarial examples
Yiwen Guo, Qizhang Li, and Hao Chen · 2020
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Individual differences among deep neural network models
Johannes Mehrer, Courtney J Spoerer, Nikolaus Kriegeskorte, and Tim C Kietzmann · 2020
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Thao Nguyen, Maithra Raghu, and Simon Kornblith · 2020
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Binary neural networks: A survey
Haotong Qin, Ruihao Gong, Xianglong Liu, Xiao Bai, Jingkuan Song, and Nicu Sebe · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Skip connections eliminate singularities
A Emin Orhan and Xaq Pitkow · 2017
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Maithra Raghu, Justin Gilmer, Jason Yosinski, and Jascha Sohl-Dickstein · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
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Depara: Deep attribution graph for deep knowledge transferability
Jie Song, Yixin Chen, Jingwen Ye, Xinchao Wang, Chengchao Shen, Feng Mao, and Mingli Song · 2020
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Similarity of neural networks with gradients
Shuai Tang, Wesley J Maddox, Charlie Dickens, Tom Diethe, and Andreas Damianou · 2020
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Graph structure of neural networks
Jiaxuan You, Jure Leskovec, Kaiming He, and Saining Xie · 2020
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Interpreting image classifiers by generating discrete masks
Hao Yuan, Lei Cai, Xia Hu, Jie Wang, and Shuiwang Ji · 2020
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Understanding adversarial examples from the mutual influence of images and perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz, and In So Kweon · 2020
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Interpreting and improving adversarial robustness of deep neural networks with neuron sensitivity
Chongzhi Zhang, Aishan Liu, Xianglong Liu, Yitao Xu, Hang Yu, Yuqing Ma, and Tianlin Li · 2020
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Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks
Ronda J Zhang and Y Ye Fred · 2020
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Adversarial example detection using latent neighborhood graph
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Scalable diverse model selection for accessible transfer learning
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Grounding representation similarity through statistical testing
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Clusterability in neural networks
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A peek into the reasoning of neural networks: Interpreting with structural visual concepts
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Extracting knowledge from deep neural networks through graph analysis
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Do vision transformers see like convolutional neural networks?
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Vision transformers for dense prediction
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Generalized shape metrics on neural representations
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Distilling holistic knowledge with graph neural networks
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Deconfounded representation similarity for comparison of neural networks
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