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Mixup is a data augmentation method that generates new data points by mixing a pair of input data.
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Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Shai Shalev-Shwartz and Shai Ben-David · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Data augmentation generative adversarial networks
Antreas Antoniou, Amos Storkey, and Harrison Edwards · 2017
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Decision-based adversarial attacks: Reliable attacks against black-box machine learning models
Wieland Brendel, Jonas Rauber, and Matthias Bethge · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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The elements of statistical learning: Data mining, inference, and prediction
Jerome H Friedman · 2017
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The robust manifold defense: Adversarial training using generative models
Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, and Alexandros G Dimakis · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 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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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
Cited alongside, same era.
Learning from between-class examples for deep sound recognition
Yuji Tokozume, Yoshitaka Ushiku, and Tatsuya Harada · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
Mixup as locally linear out-of-manifold regularization
Hongyu Guo, Yongyi Mao, and Richong Zhang · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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An introduction to variational autoencoders
Diederik P Kingma and Max Welling · 2019
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Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2019
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Data interpolating prediction: Alternative interpretation of mixup
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Cited alongside, same era.
Adversarial attacks and defences: A survey
Anirban Chakraborty, Manaar Alam, Vishal Dey, Anupam Chattopadhyay, and Debdeep Mukhopadhyay · 2018
Cited alongside, same era.
Waic, but why? generative ensembles for robust anomaly detection
Hyunsun Choi, Eric Jang, and Alexander A Alemi · 2018
Cited alongside, same era.
Inverting the generator of a generative adversarial network
Antonia Creswell and Anil Anthony Bharath · 2018
Cited alongside, same era.
Data augmentation by pairing samples for images classification
Hiroshi Inoue · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Are generative classifiers more robust to adversarial attacks?
Yingzhen Li, John Bradshaw, and Yash Sharma · 2018
Cited alongside, same era.
Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, and Sosuke Kobayashi · 2019
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Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha · 2019
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Manifold mixup: Better representations by interpolating hidden states
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, and Yoshua Bengio · 2019
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Adversarial examples: Attacks and defenses for deep learning
Xiaoyong Yuan, Pan He, Qile Zhu, and Xiaolin Li · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, and Jean-Philippe Vert · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
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E-abs: Extending the analysis-by-synthesis robust classification model to more complex image domains
An Ju and David Wagner · 2020
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Puzzle mix: Exploiting saliency and local statistics for optimal mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
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Saliencymix: A saliency guided data augmentation strategy for better regularization
AFM Uddin, Mst Monira, Wheemyung Shin, TaeChoong Chung, Sung-Ho Bae, et al · 2020
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Adversarial attacks for tabular data: Application to fraud detection and imbalanced data
Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht · 2021
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k-mixup regularization for deep learning via optimal transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, and Edward Chien · 2021
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A quantitative analysis of the robustness of neural networks for tabular data
Kavya Gupta, Beatrice Pesquet-Popescu, Fateh Kaakai, and Jean-Christophe Pesquet · 2021
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MixRL: Data mixing augmentation for regression using reinforcement learning
Seong-Hyeon Hwang and Steven Euijong Whang · 2021
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Co-mixup: Saliency guided joint mixup with supermodular diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, and Hyun Oh Song · 2021
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Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2021
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