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Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Aishwarya Agrawal, Dhruv Batra, and Devi Parikh · 2016
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Right for the right reasons: training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C Hughes, and Finale Doshi-Velez · 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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Yujia Bao, Shiyu Chang, Mo Yu, and Regina Barzilay · 2018
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Yonatan Belinkov and Yonatan Bisk · 2018
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Explaining image classifiers by counterfactual generation
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, and David Duvenaud · 2018
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith · 2018
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Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Ross and Finale Doshi-Velez · 2018
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Hierarchical interpretations for neural network predictions
Chandan Singh, W James Murdoch, and Bin Yu · 2018
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Generative image inpainting with contextual attention
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2018
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Confounding variables can degrade generalization performance of radiological deep learning models
Gradmask: Reduce overfitting by regularizing saliency
Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2019
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Underwhelming generalization improvements from controlling feature attribution
Joseph D Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, and Joseph Paul Cohen · 2019
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Deep neural network or dermatologist?
Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, and Sally Shrapnel · 2019
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Care: Class attention to regions of lesion for classification on imbalanced data
Jiaxin Zhuang, Jiabin Cai, Ruixuan Wang, Jianguo Zhang, and Weishi Zheng · 2019
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Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology
Ran Zmigrod, Sebastian J Mielke, Hanna Wallach, and Ryan Cotterell · 2019
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John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric K Oermann · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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(de) constructing bias on skin lesion datasets
Alceu Bissoto, Michel Fornaciali, Eduardo Valle, and Sandra Avila · 2019
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Learning credible deep neural networks with rationale regularization
Mengnan Du, Ninghao Liu, Fan Yang, and Xia Hu · 2019
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Learning explainable models using attribution priors
Gabriel Erion, Joseph D Janizek, Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2019
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Saliency learning: Teaching the model where to pay attention
Reza Ghaeini, Xiaoli Fern, Hamed Shahbazi, and Prasad Tadepalli · 2019
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Large scale learning of general visual representations for transfer
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2019
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Towards causal vqa: Revealing and reducing spurious correlations by invariant and covariant semantic editing
Vedika Agarwal, Rakshith Shetty, and Mario Fritz · 2020
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Counterfactual samples synthesizing for robust visual question answering
Long Chen, Xin Yan, Jun Xiao, Hanwang Zhang, Shiliang Pu, and Yueting Zhuang · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2020
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary Lipton · 2020
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Gender bias in neural natural language processing
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta · 2020
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A critic evaluation of methods for covid-19 automatic detection from x-ray images
Gianluca Maguolo and Loris Nanni · 2020
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, William Murdoch, and Bin Yu · 2020
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Distributionally robust neural networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang · 2020
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Noise or signal: The role of image backgrounds in object recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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