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We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning.
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Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell, and Jitendra Malik · 2015
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Fine-Grained Recognition Without Part Annotations
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Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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Multiple Granularity Descriptors for Fine-Grained Categorization
Dequan Wang, Zhiqiang Shen, Jie Shao, Wei Zhang, Xiangyang Xue, and Zheng Zhang · 2015
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Jason Yosinski, Jeff Clune, Thomas Fuchs, and Hod Lipson · 2015
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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The iNaturalist Species Classification and Detection Dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
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Additive Margin Softmax for Face Verification
Feng Wang, Jian Cheng, Weiyang Liu, and Haijun Liu · 2018
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CosFace: Large Margin Cosine Loss for Deep Face Recognition
Hao Wang, Yitong Wang, Zheng Zhou, Xing Ji, Dihong Gong, Jingchao Zhou, Zhifeng Li, and Wei Liu · 2018
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Machine Learning Risk Assessments in Criminal Justice Settings
Richard Berk, Drougas Berk, and Drougas · 2019
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This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Jade Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 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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Deformable Convolutional Networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 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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Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger · 2017
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Active Convolution: Learning the Shape of Convolution for Image Classification
Yunho Jeon and Junmo Kim · 2017
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SphereFace: Deep Hypersphere Embedding for Face Recognition
Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, and Le Song · 2017
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Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition
Heliang Zheng, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo · 2019
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Deformable Convnets V2: More Deformable, Better Results
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai · 2019
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Interpretable and Accurate Fine-Grained Recognition via Region Grouping
Zixuan Huang and Yin Li · 2020
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Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, and Bo Zhang · 2020
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A case-based interpretable deep learning model for classification of mass lesions in digital mammography
Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, and Cynthia Rudin · 2021
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Interpretable Image Recognition by Constructing Transparent Embedding Space
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Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature
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