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The reasonable definition of semantic interpretability presents the core challenge in explainable AI.
Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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Sanja Fidler and Ales Leonardis · 2007
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Visualizing data using t-sne
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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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Learning the compositional nature of visual object categories for recognition
Bjorn Ommer and Joachim M. Buhmann · 2009
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Part and appearance sharing: Recursive compositional models for multi-view
Long (Leo) Zhu, Yuanhao Chen, Antonio Torralba, William Freeman, and Alan Yuille · 2010
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Latent hierarchical structural learning for object detection
Long (Leo) Zhu, Yuanhao Chen, Alan Yuille, and William Freeman · 2010
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Shared parts for deformable part-based models
Patrick Ott and Mark Everingham · 2011
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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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Learning and-or templates for object recognition and detection
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Exemplar-based face parsing
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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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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Semantic part segmentation using compositional model combining shape and appearance
Jianyu Wang and Alan Yuille · 2015
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton · 2017
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Teaching compositionality to cnns
Austin Stone, Huayan Wang, Michael Stark, Yi Liu, D. Scott Phoenix, and Dileep George · 2017
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Ruth Fong and Andrea Vedaldi · 2018
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Interpretable convolutional neural networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu · 2018
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This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K. Su · 2019
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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beta-vae: Learning basic visual concepts with a constrained variational framework
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Interpretable and accurate fine-grained recognition via region grouping
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Compositional convolutional neural networks: A deep architecture with innate robustness to partial occlusion
Adam Kortylewski, Ju He, Qing Liu, and Alan Yuille · 2020
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Interpretable neural network decoupling
Yuchao Li, Rongrong Ji, Shaohui Lin, Baochang Zhang, Chenqian Yan, Yongjian Wu, Feiyue Huang, and Ling Shao · 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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