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Mixup is a popular regularization technique for training deep neural networks that improves generalization and increases robustness to certain distribution shifts.
Computational Optimal Transport: With Applications to Data Science
Gabriel Peyré and Marco Cuturi · 1935
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Moments of the Absolute Difference and Absolute Deviation of Discrete Distributions
S. K. Katti · 1960
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Introduction to Linear Optimization
Dimitris Bertsimas and John Tsitsiklis · 1997
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Algebraic topology
Allen Hatcher · 2000
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On the surprising behavior of distance metrics in high dimensional space
Charu C Aggarwal, Alexander Hinneburg, and Daniel A Keim · 2001
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Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, and Jean-Philippe Vert · 2006
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling
Filippo Santambrogio · 2015
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, and Nicolas Courty · 2018
Cited alongside, same era.
Learning Generative Models with Sinkhorn Divergences
Aude Genevay, Gabriel Peyre, and Marco Cuturi · 2018
Cited alongside, same era.
Manifold Mixup: Learning Better Representations by Interpolating Hidden States
Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Aaron Courville, Ioannis Mitliagkas, and Yoshua Bengio · 2018
Cited alongside, same era.
Speech commands: A dataset for limited-vocabulary speech recognition
Pete Warden · 2018
Cited alongside, same era.
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
Cited alongside, same era.
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, and Hyun Oh Song · 2020
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Gan-mixup: Augmenting across data manifolds for improved robustness
Jy Yong Sohn, Jaekyun Moon, Kangwook Lee, and Dimitris Papailiopoulos · 2020
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k k -variance: A clustered notion of variance
Justin Solomon, Kristjan Greenewald, and Haikady N Nagaraja · 2020
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How Does Mixup Help With Robustness and Generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2020
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Stackmix: A complementary mix algorithm, 2021
John Chen, Samarth Sinha, and Anastasios Kyrillidis · 2021
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Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
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Hongyu Guo, Yongyi Mao, and Richong Zhang · 2019
Cited alongside, same era.
Sharp asymptotic and finite-sample rates of convergence of empirical measures in wasserstein distance
Jonathan Weed and Francis Bach · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Learning with minibatch Wasserstein : asymptotic and gradient properties
Kilian Fatras, Younes Zine, Rémi Flamary, Remi Gribonval, and Nicolas Courty · 2020
Cited alongside, same era.
Torchattacks: A pytorch repository for adversarial attacks
Hoki Kim · 2020
Cited alongside, same era.
Kilian Fatras, Thibault Sejourne, Rémi Flamary, and Nicolas Courty · 2021
Closest in time.
Co-mixup: Saliency guided joint mixup with supermodular diversity
JangHyun Kim, Wonho Choo, Hosan Jeong, and Hyun Oh Song · 2021
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Unveiling the power of mixup for stronger classifiers, 2021
Zicheng Liu, Siyuan Li, Di Wu, Zhiyuan Chen, Lirong Wu, Jianzhu Guo, and Stan Z. Li · 2021
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Preventing manifold intrusion with locality: Local mixup, 2022
Raphael Baena, Lucas Drumetz, and Vincent Gripon · 2022
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