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Conventionally, AI models are thought to trade off explainability for lower accuracy.
Context-based vision system for place and object recognition
A Torralba, KP Murphy, WT Freeman, and MA Rubin · 2003
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Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, and Fernando Pereira · 2006
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The role of context in object recognition
Aude Oliva and Antonio Torralba · 2007
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Exploiting hierarchical context on a large database of object categories
Myung Jin Choi, Joseph J Lim, Antonio Torralba, and Alan S Willsky · 2010
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Adapting visual category models to new domains
Kate Saenko, Brian Kulis, Mario Fritz, and Trevor Darrell · 2010
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Domain adaptation for statistical classifiers
Hal Daumé III and Daniel Marcu · 2011
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Domain generalization via invariant feature representation
Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks
Chunshui Cao, Xianming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, et al · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor S. Lempitsky · 2015
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep transfer learning with joint adaptation networks
Mingsheng Long, Han Zhu, Jianmin Wang, and Michael I Jordan · 2016
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Deep CORAL: correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Top-down neural attention by excitation backprop
Jianming Zhang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 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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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M. Hospedales · 2017
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Unified deep supervised domain adaptation and generalization
Saeid Motiian, Marco Piccirilli, Donald A. Adjeroh, and Gianfranco Doretto · 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
RISE: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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Generalizing across domains via cross-gradient training
Shiv Shankar, Vihari Piratla, Soumen Chakrabarti, Siddhartha Chaudhuri, Preethi Jyothi, and Sunita Sarawagi · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Top-down neural attention by excitation backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Stan Sclaroff · 2018
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Adversarial complementary learning for weakly supervised object localization
Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, and Thomas Huang · 2018
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Weakly supervised instance segmentation using class peak response
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Domain randomization for transferring deep neural networks from simulation to the real world
Joshua Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan · 2017
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Excitation backprop for RNNs
Sarah Adel Bargal, Andrea Zunino, Donghyun Kim, Jianming Zhang, Vittorio Murino, and Stan Sclaroff · 2018
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach · 2018
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Yanzhao Zhou, Yi Zhu, Qixiang Ye, Qiang Qiu, and Jianbin Jiao · 2018
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Guided zoom: Questioning network evidence for fine-grained classification
Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Kate Saenko, Vittorio Murino, and Stan Sclaroff · 2019
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Domain generalization by solving jigsaw puzzles
Fabio M. Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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Episodic training for domain generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, and Timothy M. Hospedales · 2019
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Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 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, Dhruv Batra, and Devi Parikh · 2019
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Addressing model vulnerability to distributional shifts over image transformation sets
Riccardo Volpi and Vittorio Murino · 2019
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Excitation dropout: Encouraging plasticity in deep neural networks
Andrea Zunino, Sarah Adel Bargal, Pietro Morerio, Jianming Zhang, Stan Sclaroff, and Vittorio Murino · 2019
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Predicting intentions from motion: The subject-adversarial adaptation approach
Andrea Zunino, Jacopo Cavazza, Riccardo Volpi, Pietro Morerio, Andrea Cavallo, Cristina Becchio, and Vittorio Murino · 2020
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