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In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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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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One-shot learning with a hierarchical nonparametric bayesian model
Ruslan Salakhutdinov, Joshua Tenenbaum, and Antonio Torralba · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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The application of two-level attention models in deep convolutional neural network for fine-grained image classification
Tianjun Xiao, Yichong Xu, Kuiyuan Yang, Jiaxing Zhang, Yuxin Peng, and Zheng Zhang · 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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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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” why should i trust you?” explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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A roadmap for a rigorous science of interpretability
Finale Doshi-Velez and Been Kim · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition
Jianlong Fu, Heliang Zheng, and Tao Mei · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 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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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Learning multi-attention convolutional neural network for fine-grained image recognition
Heliang Zheng, Jianlong Fu, Tao Mei, and Jiebo Luo · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Explaining deep classification of time-series data with learned prototypes
Alan H Gee, Diego Garcia-Olano, Joydeep Ghosh, and David Paydarfar · 2019
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Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
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Black box explanation by learning image exemplars in the latent feature space
Riccardo Guidotti, Anna Monreale, Stan Matwin, and Dino Pedreschi · 2019
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Interpretable image recognition with hierarchical prototypes
Peter Hase, Chaofan Chen, Oscar Li, and Cynthia Rudin · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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Oscar Li, Hao Liu, Chaofan Chen, and Cynthia Rudin · 2018
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Exploring linear relationship in feature map subspace for convnets compression
Dong Wang, Lei Zhou, Xueni Zhang, Xiao Bai, and Jun Zhou · 2018
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Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers
Jianbo Ye, Xin Lu, Zhe Lin, and James Z Wang · 2018
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Nisp: Pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis · 2018
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Interpretable basis decomposition for visual explanation
Bolei Zhou, Yiyou Sun, David Bau, and Antonio Torralba · 2018
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Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu · 2018
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One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques
Vijay Arya, Rachel KE Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C Hoffman, Stephanie Houde, Q Vera Liao, Ronny Luss, Aleksandra Mojsilović, et al · 2019
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Towards optimal structured cnn pruning via generative adversarial learning
Shaohui Lin, Rongrong Ji, Chenqian Yan, Baochang Zhang, Liujuan Cao, Qixiang Ye, Feiyue Huang, and David Doermann · 2019
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
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Interpretable and steerable sequence learning via prototypes
Yao Ming, Panpan Xu, Huamin Qu, and Liu Ren · 2019
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Pruning convolutional neural networks for resource efficient inference
P Molchanov, S Tyree, T Karras, T Aila, and J Kautz · 2019
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Taking a hint: Leveraging explanations to make vision and language models more grounded
Ramprasaath R Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, and Devi Parikh · 2019
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Gaining free or low-cost interpretability with interpretable partial substitute
Tong Wang · 2019
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On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2019
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Interpretable deep models for cardiac resynchronisation therapy response prediction
Esther Puyol-Antón, Chen Chen, James R Clough, Bram Ruijsink, Baldeep S Sidhu, Justin Gould, Bradley Porter, Marc Elliott, Vishal Mehta, Daniel Rueckert, et al · 2020
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There and back again: Revisiting backpropagation saliency methods
Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, and Andrea Vedaldi · 2020
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