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Awesome Mixup Methods for Supervised Learning

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We summarize fundamental mixup methods proposed for supervised visual representation learning from two aspects: sample mixup policy and label mixup policy. Then, we summarize mixup techniques used in downstream tasks. The list of awesome mixup methods is summarized in chronological order and is on updating. And we will add more papers according to Awesome-Mix.

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Sample Mixup Methods

Pre-defined Policies

  • mixup: Beyond Empirical Risk Minimization
    Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
    ICLR’2018 [Paper] [Code]

    MixUp Framework

  • Between-class Learning for Image Classification
    Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
    CVPR’2018 [Paper] [Code]

    BC Framework

  • MixUp as Locally Linear Out-Of-Manifold Regularization
    Hongyu Guo, Yongyi Mao, Richong Zhang
    AAAI’2019 [Paper]

    AdaMixup Framework

  • CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
    Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo
    ICCV’2019 [Paper] [Code]

    CutMix Framework

  • Manifold Mixup: Better Representations by Interpolating Hidden States
    Vikas Verma, Alex Lamb, Christopher Beckham, Amir Najafi, Ioannis Mitliagkas, David Lopez-Paz, Yoshua Bengio
    ICML’2019 [Paper] [Code]

    ManifoldMix Framework

  • Improved Mixed-Example Data Augmentation
    Cecilia Summers, Michael J. Dinneen
    WACV’2019 [Paper] [Code]

    MixedExamples Framework

  • FMix: Enhancing Mixed Sample Data Augmentation
    Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare
    Arixv’2020 [Paper] [Code]

    FMix Framework

  • SmoothMix: a Simple Yet Effective Data Augmentation to Train Robust Classifiers
    Jin-Ha Lee, Muhammad Zaigham Zaheer, Marcella Astrid, Seung-Ik Lee
    CVPRW’2020 [Paper] [Code]

    SmoothMix Framework

  • PatchUp: A Regularization Technique for Convolutional Neural Networks
    Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar
    Arxiv’2020 [Paper] [Code]

    PatchUp Framework

  • GridMix: Strong regularization through local context mapping
    Kyungjune Baek, Duhyeon Bang, Hyunjung Shim
    Pattern Recognition’2021 [Paper] [Code]

    GridMixup Framework

  • ResizeMix: Mixing Data with Preserved Object Information and True Labels
    Jie Qin, Jiemin Fang, Qian Zhang, Wenyu Liu, Xingang Wang, Xinggang Wang
    Arixv’2020 [Paper] [Code]

    ResizeMix Framework

  • Where to Cut and Paste: Data Regularization with Selective Features
    Jiyeon Kim, Ik-Hee Shin, Jong-Ryul, Lee, Yong-Ju Lee
    ICTC’2020 [Paper] [Code]

    FocusMix Framework

  • AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
    Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
    ICLR’2020 [Paper] [Code]

    AugMix Framework

  • DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness
    Ryuichiro Hataya, Hideki Nakayama
    Arxiv’2021 [Paper]

    DJMix Framework

  • PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures
    Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt
    Arxiv’2021 [Paper] [Code]

    PixMix Framework

  • StyleMix: Separating Content and Style for Enhanced Data Augmentation
    Minui Hong, Jinwoo Choi, Gunhee Kim
    CVPR’2021 [Paper] [Code]

    StyleMix Framework

  • Domain Generalization with MixStyle
    Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang
    ICLR’2021 [Paper] [Code]

    MixStyle Framework

  • On Feature Normalization and Data Augmentation
    Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger
    CVPR’2021 [Paper] [Code]

    MoEx Framework

  • Guided Interpolation for Adversarial Training
    Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama
    ArXiv’2021 [Paper]

    GIF Framework

  • Observations on K-image Expansion of Image-Mixing Augmentation for Classification
    Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi
    IEEE Access’2021 [Paper] [Code]

    DCutMix Framework

  • Noisy Feature Mixup
    Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney
    ICLR’2022 [Paper] [Code]

    NFM Framework

  • Preventing Manifold Intrusion with Locality: Local Mixup
    Raphael Baena, Lucas Drumetz, Vincent Gripon
    EUSIPCO’2022 [Paper] [Code]

    LocalMix Framework

  • RandomMix: A mixed sample data augmentation method with multiple mixed modes
    Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie
    ArXiv’2022 [Paper]

    RandomMix Framework

  • SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation
    Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika, Mahmoud Melkemi
    ArXiv’2022 [Paper] [Code]

    SuperpixelGridCut Framework

  • AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance
    Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie
    ICME’2022 [Paper]

    AugRmixAT Framework

  • A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
    Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
    NIPS’2022 [Paper] [Code]

    MSDA Framework

  • RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness
    Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H.S. Torr, Puneet K. Dokania
    NIPS’2022 [Paper] [Code]

    RegMixup Framework

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Saliency-guided Policies

  • SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
    A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae
    ICLR’2021 [Paper] [Code]

    SaliencyMix Framework

  • Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification
    Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, Marios Savvides
    ICASSP’2020 [Paper] [Code]

    AttentiveMix Framework

  • SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
    Shaoli Huang, Xinchao Wang, Dacheng Tao
    AAAI’2021 [Paper] [Code]

    SnapMix Framework

  • Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
    Hao Li, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
    VCIP’2020 [Paper]

    AttributeMix Framework

  • On Adversarial Mixup Resynthesis
    Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal
    NIPS’2019 [Paper] [Code]

    AMR Framework

  • Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
    Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
    ArXiv’2019 [Paper]

    Pani VAT Framework

  • AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
    Jianchao Zhu, Liangliang Shi, Junchi Yan, Hongyuan Zha
    ECCV’2020 [Paper]

    AutoMix Framework

  • PuzzleMix: Exploiting Saliency and Local Statistics for Optimal Mixup
    Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
    ICML’2020 [Paper] [Code]

    PuzzleMix Framework

  • Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
    Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
    ICLR’2021 [Paper] [Code]

    Co-Mixup Framework

  • SuperMix: Supervising the Mixing Data Augmentation
    Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi
    CVPR’2021 [Paper] [Code]

    SuperMix Framework

  • Evolving Image Compositions for Feature Representation Learning
    Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez
    BMVC’2021 [Paper]

    PatchMix Framework

  • StackMix: A complementary Mix algorithm
    John Chen, Samarth Sinha, Anastasios Kyrillidis
    UAI’2022 [Paper]

    StackMix Framework

  • SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
    Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, Heechul Jung
    Sensor’2021 [Paper]

    SalfMix Framework

  • k-Mixup Regularization for Deep Learning via Optimal Transport
    Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
    ArXiv’2021 [Paper]

    k-Mixup Framework

  • AlignMix: Improving representation by interpolating aligned features
    Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
    CVPR’2022 [Paper] [Code]

    AlignMix Framework

  • AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
    Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li
    ECCV’2022 [Paper] [Code]

    AutoMix Framework

  • Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
    Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li
    Arxiv’2021 [Paper] [Code]

    SAMix Framework

  • ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
    Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran
    Arxiv’2022 [Paper]

    ScoreMix Framework

  • RecursiveMix: Mixed Learning with History
    Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang
    NIPS’2022 [Paper] [Code]

    RecursiveMix Framework

  • Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
    Remy Sun, Clement Masson, Gilles Henaff, Nicolas Thome, Matthieu Cord.
    ICPR’2022 [Paper]

    SciMix Framework

  • TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation
    Tsz-Him Cheung, Dit-Yan Yeung.<\br> OpenReview’2023 [Paper]

    TransformMix Framework

  • GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
    Minsoo Kang, Suhyun Kim
    AAAI’2023 [Paper]

    GuidedMixup Framework

  • MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
    Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu
    ICLR’2023 [Paper]

    MixPro Framework

  • Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
    Minh-Long Luu, Zeyi Huang, Eric P.Xing, Yong Jae Lee, Haohan Wang
    2nd Practical-DL Workshop @ AAAI’23 [Paper] [Code]

    R-Mix and R-LMix Framework

  • SMMix: Self-Motivated Image Mixing for Vision Transformers
    Mengzhao Chen, Mingbao Lin, ZhiHang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji
    ICCV’2023 [Paper] [Code]

    SMMix Framework

  • Teach me how to Interpolate a Myriad of Embeddings
    Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
    Arxiv’2022 [Paper]

    MultiMix Framework

  • GradSalMix: Gradient Saliency-Based Mix for Image Data Augmentation
    Tao Hong, Ya Wang, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Jinwen Ma
    ICME’2023 [Paper]

    GradSalMix Framework

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Label Mixup Methods

  • mixup: Beyond Empirical Risk Minimization
    Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
    ICLR’2018 [Paper] [Code]

  • CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
    Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo
    ICCV’2019 [Paper] [Code]

  • Metamixup: Learning adaptive interpolation policy of mixup with metalearning
    Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen
    TNNLS’2021 [Paper]

    MetaMixup Framework

  • Mixup Without Hesitation
    Hao Yu, Huanyu Wang, Jianxin Wu
    ICIG’2022 [Paper] [Code]

  • Combining Ensembles and Data Augmentation can Harm your Calibration
    Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
    ICLR’2021 [Paper] [Code]

    CAMixup Framework

  • Combining Ensembles and Data Augmentation can Harm your Calibration
    Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng
    NIPS’2021 [Paper] [Code]

    TokenLabeling Framework

  • Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
    Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang
    AAAI’2022 [Paper]

    Saliency Grafting Framework

  • TransMix: Attend to Mix for Vision Transformers
    Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai
    CVPR’2022 [Paper] [Code]

    TransMix Framework

  • GenLabel: Mixup Relabeling using Generative Models
    Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee
    ArXiv’2022 [Paper]

    GenLabel Framework

  • Decoupled Mixup for Data-efficient Learning
    Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
    NIPS’2023 [Paper] [Code]

    DecoupleMix Framework

  • TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
    Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu
    ECCV’2022 [Paper] [Code]

    TokenMix Framework

  • Optimizing Random Mixup with Gaussian Differential Privacy
    Donghao Li, Yang Cao, Yuan Yao
    arXiv’2022 [Paper]

  • TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
    Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim
    NIPS’2022 [Paper] [Code]

    TokenMixup Framework

  • Token-Label Alignment for Vision Transformers
    Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
    arXiv’2022 [Paper] [Code]

    TL-Align Framework

  • LUMix: Improving Mixup by Better Modelling Label Uncertainty
    Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille, Philip Torr, Song Bai
    arXiv’2022 [Paper] [Code]

    LUMix Framework

  • MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
    Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi
    arXiv’2022 [Paper]

    MixupE Framework

  • Infinite Class Mixup
    Thomas Mensink, Pascal Mettes
    arXiv’2023 [Paper]

    IC-Mixup Framework

  • Semantic Equivariant Mixup
    Zongbo Han, Tianchi Xie, Bingzhe Wu, Qinghua Hu, Changqing Zhang
    arXiv’2023 [Paper]

    SEM Framework

  • RankMixup: Ranking-Based Mixup Training for Network Calibration
    Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham
    ICCV’2023 [Paper] [Code]

    RankMixup Framework

  • G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
    Xingyu Li, Bo Tang
    arXiv’2023 [Paper]

    G-Mix Framework

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Analysis of Mixup

  • Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak.

    • On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. [NIPS’2019] [code]

    Framework

  • Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert.

    Framework

  • Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou.

    • How Does Mixup Help With Robustness and Generalization? [ICLR’2021]

    Framework

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

    • Towards Understanding the Data Dependency of Mixup-style Training. [ICLR’2022] [code]

    Framework

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

    • When and How Mixup Improves Calibration. [ICML’2022]

    Framework

  • Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao.

    • Over-Training with Mixup May Hurt Generalization. [ICLR’2023]

    Framework

  • Junsoo Oh, Chulhee Yun.

    • Provable Benefit of Mixup for Finding Optimal Decision Boundaries. [ICML’2023]

  • Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang.

    • On the Pitfall of Mixup for Uncertainty Calibration. [CVPR’2023]

  • Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga.

    • Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study. [WACV’2023] [code]

  • Soyoun Won, Sung-Ho Bae, Seong Tae Kim.

    • Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability. [arXiv’2023]

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Survey

  • A survey on Image Data Augmentation for Deep Learning
    Connor Shorten and Taghi Khoshgoftaar
    Journal of Big Data’2019 [Paper]

  • Survey: Image Mixing and Deleting for Data Augmentation
    Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, Ajmal Mian
    ArXiv’2021 [Paper] [Code]

  • An overview of mixing augmentation methods and augmentation strategies
    Dominik Lewy and Jacek Ma ́ndziuk
    Artificial Intelligence Review’2022 [Paper]

  • Image Data Augmentation for Deep Learning: A Survey
    Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, Furao Shen
    ArXiv’2022 [Paper]

  • A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
    Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang
    ArXiv’2022 [Paper] [Code]

Contribution

Feel free to send pull requests to add more links with the following Markdown format. Notice that the Abbreviation, the code link, and the figure link are optional attributes. Current contributors include: Siyuan Li (@Lupin1998) and Zicheng Liu (@pone7).

* **TITLE**<br>
*AUTHER*<br>
PUBLISH'YEAR [[Paper](link)] [[Code](link)]
   <details close>
   <summary>ABBREVIATION Framework</summary>
   <p align="center"><img width="90%" src="link_to_image" /></p>
   </details>