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https://github.com/demoleiwang/awesome-mixup

A paper list about mixup.
https://github.com/demoleiwang/awesome-mixup

List: awesome-mixup

awesome-mixup mixup papers

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A paper list about mixup.

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# awesome-mixup



This repo is a collection of AWESOME things about mixup, including papers, code, etc. Feel free to star and fork. We borrow a lot from [openmixup](https://github.com/Westlake-AI/openmixup), [Awesome-Mixup](https://github.com/Westlake-AI/Awesome-Mixup), [awesome-domain-adaptation](https://github.com/zhaoxin94/awesome-domain-adaptation), and [PromptPapers](https://github.com/thunlp/PromptPapers).



Some of these papers are summarized with tables in Google Sheet. Please find the link here: [Summary(Restricted)](https://docs.google.com/spreadsheets/d/1U34nbDFy7GRU78ZKmgourSDZrFngc4QN_fIvgfSchek/edit?usp=sharing)



![](https://img.shields.io/github/last-commit/demoleiwang/awesome-mixup?color=green) ![](https://img.shields.io/badge/PaperNumber-52-brightgreen) ![](https://img.shields.io/badge/PRs-Welcome-red) 



### Basics

This section contains the exploration on the improvements aspects of raw mixup.



1. **[[mixup'18]](https://arxiv.org/abs/1710.09412)** mixup: Beyond Empirical Risk Minimization. ICLR 2018. [[code](https://github.com/facebookresearch/mixup-cifar10)].



    *Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz.*



1. **[[Manifold Mixup'19]](https://arxiv.org/abs/1806.05236)** Manifold Mixup: Better Representations by Interpolating Hidden States. ICML 2019. [[code](https://github.com/vikasverma1077/manifold_mixup)] 



    *Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio*



1. **[[AdaMixup'19]](https://arxiv.org/abs/1809.02499)** MixUp as Locally Linear Out-Of-Manifold Regularization. AAAI 2019. 



    *Hongyu Guo, Yongyi Mao, Richong Zhang.*



1. **[[CutMix'19]](https://arxiv.org/abs/1905.04899)** CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features. ICCV 2019. [[code](https://github.com/clovaai/CutMix-PyTorch)] 



    *Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo.*



1. **[[AugMix'20]](https://arxiv.org/abs/1912.02781)** AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR 2020. [[code](https://github.com/google-research/augmix)] 



    *Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan.*      

    

1. **[[SnapMix'21]](https://arxiv.org/pdf/2012.04846)** SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data. AAAI 2021. [[code](https://github.com/Shaoli-Huang/SnapMix.git)] 



    *Shaoli Huang, Xinchao Wang, Dacheng Tao.* 

    

1. **[[PuzzleMix'20]](https://arxiv.org/abs/2009.06962)** Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup. ICML 2020. [[code](https://github.com/snu-mllab/PuzzleMix)] 



    *Jang-Hyun Kim, Wonho Choo, Hyun Oh Song.*



1. **[[SaliencyMix'21]](https://arxiv.org/abs/2006.01791)** SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization. ICLR 2021. [[code](x)] 



    *A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae.*



1. **[[CoMixup'21]](https://arxiv.org/abs/2102.03065)** Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity. ICLR 2021. [[code](https://github.com/snu-mllab/Co-Mixup)] 



    *Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song.*



1. **[[NFM'22]](https://arxiv.org/pdf/2110.02180.pdf)** Noisy Feature Mixup. ICLR 2022. [[code](https://github.com/erichson/NFM)] 



    *Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney*



1. **[[AlignMix'22]](https://arxiv.org/abs/2103.15375)** AlignMix: Improving representation by interpolating aligned features. CVPR 2022. [[code](https://github.com/shashankvkt/AlignMixup_CVPR22)] 



    *Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis.*

    

1. **[[TransMix'22]](https://arxiv.org/abs/2111.09833)** TransMix: Attend to Mix for Vision Transformers. CVPR 2022. [[code](https://github.com/Beckschen/TransMix)] 



    *Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai.*



1. **[[GenLabel'22]](https://proceedings.mlr.press/v162/sohn22a/sohn22a.pdf)** GenLabel: Mixup Relabeling using Generative Models. ICML 2022. [[code](https://github.com/UW-Madison-Lee-Lab/GenLabel_official)] 



    *Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee.*



1. **[[VLMixer'22]](https://arxiv.org/pdf/2206.08919.pdf)** VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix. ICML 2022. [[code](x)] 



    *Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo*



1. **[[AutoMix'22]](https://arxiv.org/abs/2103.13027)** AutoMix: Unveiling the Power of Mixup for Stronger Classifiers. ECCV 2022. [[code](https://github.com/Westlake-AI/openmixup)]



    *Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li.*



1. **[[TokenMix'22]](https://arxiv.org/abs/2207.08409)** TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers. ECCV 2022. [[code](https://github.com/Sense-X/TokenMix)]



    *Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu*



1. **[[MDD'22]](https://openreview.net/pdf?id=ieNJYujcGDO)** Towards Understanding the Data Dependency of Mixup-style Training. ICLR 2022. [[code](https://github.com/2014mchidamb/Mixup-Data-Dependency)] 



    *Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge.* 



1. **[[WH-Mixup'22]](https://proceedings.mlr.press/v162/zhang22f.html)** When and How Mixup Improves Calibration. ICML 2022. 



   *Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou.* 



1. **[[RegMixup'22]](https://arxiv.org/pdf/2206.14502)** RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness. NeurIPS 2022. [[code](https://github.com/FrancescoPinto/RegMixup)] 



    *Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania.* 



1. **[[RecursiveMix'22]](https://arxiv.org/pdf/2203.06844)** RecursiveMix: Mixed Learning with History. NeurIPS 2022.  [[code](https://github.com/megvii-research/RecursiveMix)] 



    *Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang.* 



1. **[[MSDA'22]](https://arxiv.org/pdf/2208.09913)** A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective. NeurIPS 2022. [[code](https://github.com/naver-ai/hmix-gmix)] 



    *Chanwoo Park, Sangdoo Yun, Sanghyuk Chun.* 



### Contrastive Learning with Mixup



1. **[[MixCo'20]](https://arxiv.org/abs/2010.06300)** MixCo: Mix-up Contrastive Learning for Visual Representation. NeurIPSW. [[code](https://github.com/Lee-Gihun/MixCo-Mixup-Contrast)] 



    *MixCo: Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun.*

    

1. **[[MoCHi'20]](https://arxiv.org/abs/2010.01028)** Hard Negative Mixing for Contrastive Learning. NeurIPS 2020. [[code](https://europe.naverlabs.com/mochi)] 



    *Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus.*

    

1. **[[i-Mix'21]](https://arxiv.org/abs/2010.08887)** i-Mix A Domain-Agnostic Strategy for Contrastive Representation Learning. ICLR 2021. [[code](https://github.com/kibok90/imix)] 



    *Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee.*

    

1. **[[FT'19]](https://arxiv.org/abs/2108.02982)** Improving Contrastive Learning by Visualizing Feature Transformation. ICCV 2019 (Oral). [[code](https://github.com/DTennant/CL-Visualizing-Feature-Transformation)] 



    *Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen.*

    

1. **[[Core-tuning'21]](https://arxiv.org/abs/2102.06605)** Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning.  NeurIPS 2021. [[code](https://github.com/vanint/core-tuning)] 



    *Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng.*

    

1. **[[MixSiam'22]](https://arxiv.org/pdf/2111.02679.pdf)** MixSiam: A Mixture-based Approach to Self-supervised Representation Learning. AAAI 2022. 



    *Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du.*

    

1. **[[Un-Mix'22]](https://arxiv.org/abs/2003.05438)** Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation. AAAI 2022. [[code](https://github.com/szq0214/Un-Mix)] 



    *Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing.*

    

1. **[[Metrix'22]](https://www.semanticscholar.org/reader/a7721bf626f4996117dbb88b385be2e12462e7e6)** It Takes Two to Tango: Mixup for Deep Metric Learning. ICLR 2022. [[code](https://github.com/billpsomas/Metrix_ICLR22)] 



    *Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis.*



1. **[[ProGCL'22]](https://arxiv.org/abs/2110.02027)** ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning. ICML 2022. [[code](https://github.com/junxia97/ProGCL)] 



    *Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li.*



1. **[[M-Mix'22]]()** M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning. KDD 2022. [[code](https://github.com/Sherrylone/m-mix)] 



    *Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang.* 



### Semi-supervised Learning with Mixup



1. **[[ICT'19]](https://arxiv.org/abs/1903.03825)** Interpolation Consistency Training for Semi-Supervised Learning. IJCAI 2019. [[code](https://github.com/vikasverma1077/ICT)] 



    *Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Yoshua Bengio, David Lopez-Paz*



1. **[[MixMatch'19]](https://arxiv.org/abs/1905.02249)** MixMatch: A Holistic Approach to Semi-Supervised Learning. NeurIPS 2019. [[code](https://github.com/google-research/mixmatch)] 



    *David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel.*



1. **[[P3MIX'22]](https://openreview.net/pdf?id=NH29920YEmj)** Who Is Your Right Mixup Partner in Positive and Unlabeled Learning. ICLR 2022. [[code](x)] 



    *Changchun Li, Ximing Li, Lei Feng, Jihong Ouyang.* 



    

    

### Mixup in NLP



1. **[[mixup-text'19]](https://arxiv.org/pdf/1905.08941)** Augmenting Data with Mixup for Sentence Classification: An Empirical Study. arXiv 2019. [[code](https://github.com/xashru/mixup-text/blob/master/README.md)] 



    *Hongyu Guo, Yongyi Mao, Richong Zhang.* 



1. **[[TMix'20]](https://aclanthology.org/2020.acl-main.194.pdf)** MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification. ACL 2020. [[code]](https://github.com/SALT-NLP/MixText)



   *Jiaao Chen, Zichao Yang, and Diyi Yang.*



1. **[[Mixup-Transformer'20]](https://arxiv.org/pdf/2010.02394.pdf)** Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks. COLING 2020. 



    *Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, Lifang He.* 



1. **[[AdvAug'20]](https://arxiv.org/pdf/2006.11834)** AdvAug: Robust Adversarial Augmentation for Neural Machine Translation. ACL 2020. 



    *Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein.* 



1. **[[SL'20]](https://aclanthology.org/2020.emnlp-main.447.pdf)** Sequence-Level Mixed Sample Data Augmentation. EMNLP 2020.



   *Demi Guo, Yoon Kim, Alexander Rush.*

   

1. **[[BRMC'21]](https://aclanthology.org/2021.findings-acl.137.pdf)** Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning. ACL 2021.



   *Chenglei Si, Zhengyan Zhang, Fanchao Qi, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun.*



1. **[[HYPMIX'21]](https://aclanthology.org/2021.emnlp-main.776.pdf)** HYPMIX: Hyperbolic Interpolative Data Augmentation. EMNLP 2021. [[code](https://github.com/caisa-lab/hypmix-emnlp)] 



    *Ramit Sawhney, Megh Thakkar, Shivam Agarwal, Di Jin, Diyi Yang, Lucie Flek* 



1. **[[SSMix'21]](https://arxiv.org/pdf/2106.08062.pdf)** SSMix: Saliency-Based Span Mixup for Text Classification. ACL Findings 2021. [[code](https://github.com/clovaai/ssmix)] 



    *Soyoung Yoon, Gyuwan Kim, Kyumin Park* 



1. **[[Multilingual Mix'22]](https://aclanthology.org/2022.acl-long.282.pdf)** Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation. ACL 2022. 



    *Yong Cheng, Ankur Bapna, Orhan Firat, Yuan Cao, Pidong Wang, and Wolfgang Macherey* 



1. **[[DMix'22]](https://aclanthology.org/2022.acl-short.67/)** DMIX: Adaptive Distance-aware Interpolative Mixup. ACL 2022. (SHORT) 



    *Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek* 



1. **[[STEMM'22]](https://aclanthology.org/2022.acl-long.486.pdf)** STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation. ACL 2022. [[code](https://github.com/ictnlp/STEMM)] 



    *Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang.* 

    

1. **[[CsaNMT'22]](https://arxiv.org/pdf/2204.06812.pdf)** Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation. ACL 2022. [[code](https://github.com/pemywei/csanmt)] 



    *Xiangpeng Wei, Heng Yu, Yue Hu, Rongxiang Weng, Weihua Luo, Jun Xie, Rong Jin.*



1. **[[AUMS'22]](https://aclanthology.org/2022.acl-long.368.pdf)** On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency. ACL 2022. [[code](https://github.com/shreydesai/calibration)] 



    *Seo Yeon Park and Cornelia Caragea.* 

    

1. **[[XAIMix'22]](https://dl.acm.org/doi/pdf/10.1145/3533048)** Explainability-based mix-up approach for text data augmentation. TKDD. 



    *Soonki Kwon , Younghoon Lee.* 



1. **[[TreeMix'22]](https://aclanthology.org/2022.naacl-main.385.pdf)** TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding. NAACL 2022. [[code](https://github.com/magiccircuit/treemix)] 



    *Le Zhang, Zichao Yang, Diyi Yang.* 



1. **[[X-Mixup'22]](https://openreview.net/forum?id=OjPmfr9GkVv)** Enhancing Cross-lingual Transfer by Manifold Mixup. ICLR 2022. [[code](https://github.com/yhy1117/X-Mixup)] 



    *Huiyun Yang, Huadong Chen, Hao Zhou, Lei Li.* 



### Other Application



1. **[[SMFM'22]](https://arxiv.org/pdf/2206.08661.pdf)** Boosting Factorization Machines via Saliency-Guided Mixup. 2022. [[code](https://github.com/Daftstone/SMFM)] 



    *Chenwang Wu, Defu Lian, Yong Ge, Min Zhou, Enhong Chen, Dacheng Tao.* 



1. **[[MIX-TS]](https://www.sciencedirect.com/science/article/pii/S0167865522000502)** Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series. PRL 2022. [[code](https://github.com/Wickstrom/MixupContrastiveLearning)] 



    *Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen.*