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Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Direct and indirect effects
Judea Pearl · 2001
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Causality
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Imagenet classification with deep convolutional neural networks
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Universal estimation of directed information
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Auto-encoding variational bayes, 2013
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Domain-adversarial training of neural networks
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Improved regularization of convolutional neural networks with cutout
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Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Counterfactual fairness
Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva · 2017
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The effectiveness of data augmentation in image classification using deep learning
Luis Perez and Jason Wang · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2017
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Deep domain generalization via conditional invariant adversarial networks
Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, and Dacheng Tao · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, ZHANGJIE CAO, Jianmin Wang, and Michael I Jordan · 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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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Invariant risk minimization, 2019
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Detecting bias with generative counterfactual face attribute augmentation, 2019
Emily Denton, Ben Hutchinson, Margaret Mitchell, and Timnit Gebru · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M. Khoshgoftaar · 2019
Counterfactual data augmentation using locally factored dynamics
Silviu Pitis, Elliot Creager, and Animesh Garg · 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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Data augmentation for discrimination prevention and bias disambiguation
Shubham Sharma, Yunfeng Zhang, Jesús M. Ríos Aliaga, Djallel Bouneffouf, Vinod Muthusamy, and Kush R. Varshney · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le · 2020
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Training confounder-free deep learning models for medical applications
Qingyu Zhao, Ehsan Adeli, and Kilian M Pohl · 2020
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Model patching: Closing the subgroup performance gap with data augmentation
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Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness
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Information theoretic causal effect quantification
Aleksander Wieczorek and Volker Roth · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Sanghyuk Chun, Seong Joon Oh, Youngjoon Yoo, and Junsuk Choe · 2019
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Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology, 2019
Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, and Ryan Cotterell · 2019
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A causal view of compositional zero-shot recognition
Yuval Atzmon, Felix Kreuk, Uri Shalit, and Gal Chechik · 2020
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Estimating the effects of continuous-valued interventions using generative adversarial networks
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Karan Goel, Albert Gu, Yixuan Li, and Christopher Re · 2021
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Selecting data augmentation for simulating interventions
Maximilian Ilse, Jakub M Tomczak, and Patrick Forré · 2021
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Anchor regression: Heterogeneous data meet causality
Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, Jonas Peters, et al · 2021
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Counterfactual generative networks
Axel Sauer and Andreas Geiger · 2021
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Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio · 2021
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On disentangled representations learned from correlated data
Frederik Träuble, Elliot Creager, Niki Kilbertus, Francesco Locatello, Andrea Dittadi, Anirudh Goyal, Bernhard Schölkopf, and Stefan Bauer · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius Von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Counterfactual zero-shot and open-set visual recognition
Zhongqi Yue, Tan Wang, Qianru Sun, Xian-Sheng Hua, and Hanwang Zhang · 2021
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Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals
Saloni Dash, Vineeth N Balasubramanian, and Amit Sharma · 2022
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Disentanglement and generalization under correlation shifts
Christina M Funke, Paul Vicol, Kuan-Chieh Wang, Matthias Kuemmerer, Richard Zemel, and Matthias Bethge · 2022
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On causally disentangled representations
Abbavaram Gowtham Reddy, Benin L Godfrey, and Vineeth N Balasubramanian · 2022
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Out-of-distribution generalization with causal invariant transformations
Ruoyu Wang, Mingyang Yi, Zhitang Chen, and Shengyu Zhu · 2022
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Image data augmentation for deep learning: A survey, 2022
Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, and Furao Shen · 2022
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