Westlake-AI / Awesome-Mixup

Awesome List of Mixup Augmentation Papers for Visual Representation Learning

Awesome-Mixup

Introduction

We summarize awesome mixup data augmentation methods for visual representation learning in various scenarios.

The list of awesome mixup augmentation methods is summarized in chronological order and is on updating. The main branch is modified according to Awesome-Mixup in OpenMixup and Awesome-Mix, and we are working on a comperhensive survey on mixup augmentations. We first summarize fundamental mixup methods from two aspects: sample mixup policy and label mixup policy. Then, we summarize mixup techniques for self- and semi-supervised learning and various downstream tasks.

• To find related papers and their relationships, check out Connected Papers, which visualizes the academic field in a graph representation.
• To export BibTeX citations of papers, check out ArXiv or Semantic Scholar of the paper for professional reference formats.

Table of Contents

• Awesome-Mixup
  • Introduction
  • Table of Contents
  • Fundermental Methods
    • Sample Mixup Methods
      • Pre-defined Policies
      • Adaptive Policies
    • Label Mixup Methods
  • Mixup for Self-supervised Learning
  • Mixup for Semi-supervised Learning
  • Mixup for Regression
  • Mixup for Robustness
  • Mixup for Multi-modality
  • Analysis of Mixup
  • Natural Language Processing
  • Graph Representation Learning
  • Survey
  • Benchmark
  • Contribution
  • License
  • Acknowledgement
  • Related Project

Fundermental Methods

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

  

• ContextMix: A context-aware data augmentation method for industrial visual inspection systems
  Hyungmin Kim, Donghun Kim, Pyunghwan Ahn, Sungho Suh, Hansang Cho, Junmo Kim
  EAAI'2024 [Paper]

  ConvtextMix Framework

  

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Adaptive 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] [Code]

  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

  

• Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
  Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
  NeurIPS'2023 [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

  

• LGCOAMix: Local and Global Context-and-Object-Part-Aware Superpixel-Based Data Augmentation for Deep Visual Recognition
  Fadi Dornaika, Danyang Sun
  TIP'2023 [Paper] [Code]

  LGCOAMix Framework

  

• Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
  Minsoo Kang, Minkoo Kang, Suhyun Kim
  AAAI'2024 [Paper]

  Catch-Up-Mix Framework

  

• Adversarial AutoMixup
  Huafeng Qin, Xin Jin, Yun Jiang, Mounim A. El-Yacoubi, Xinbo Gao
  ICLR'2024 [Paper] [Code]

  AdAutoMix 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

  

• Harnessing Hard Mixed Samples with Decoupled Regularizer
  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
  UAI'2023 [Paper] [Code]

  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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Mixup for Self-supervised Learning

• MixCo: Mix-up Contrastive Learning for Visual Representation
  Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun
  NIPSW'2020 [Paper] [Code]

  MixCo Framework

  

• Hard Negative Mixing for Contrastive Learning
  Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus
  NIPS'2020 [Paper] [Code]

  MoCHi Framework

  

• i-Mix A Domain-Agnostic Strategy for Contrastive Representation Learning
  Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
  ICLR'2021 [Paper] [Code]

  i-Mix Framework

  

• Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation
  Zhiqiang Shen, Zechun Liu, Zhuang Liu, Marios Savvides, Trevor Darrell, Eric Xing
  AAAI'2022 [Paper] [Code]

  Un-Mix Framework

  

• Beyond Single Instance Multi-view Unsupervised Representation Learning
  Xiangxiang Chu, Xiaohang Zhan, Xiaolin Wei
  BMVC'2022 [Paper]

  BSIM Framework

  

• Improving Contrastive Learning by Visualizing Feature Transformation
  Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen
  ICCV'2021 [Paper] [Code]

  FT Framework

  

• Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning
  Jingwei Liu, Yi Gu, Shentong Mo, Zhun Sun, Shumin Han, Jiafeng Guo, Xueqi Cheng
  OpenReview'2021 [Paper]

  PCEA Framework

  

• Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing
  Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das
  NIPS'2021 [Paper] [Code]

  CoMix 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

  

• MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
  Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du
  OpenReview'2021 [Paper]

  MixSiam Framework

  

• Mix-up Self-Supervised Learning for Contrast-agnostic Applications
  Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann
  ICME'2021 [Paper]

  MixSSL Framework

  

• Towards Domain-Agnostic Contrastive Learning
  Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le
  ICML'2021 [Paper]

  DACL Framework

  

• Center-wise Local Image Mixture For Contrastive Representation Learning
  Hao Li, Xiaopeng Zhang, Hongkai Xiong
  BMVC'2021 [Paper]

  CLIM Framework

  

• Contrastive-mixup Learning for Improved Speaker Verification
  Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke
  ICASSP'2022 [Paper]

  Mixup Framework

  

• ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
  Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li
  ICML'2022 [Paper] [Code]

  ProGCL Framework

  

• M-Mix: Generating Hard Negatives via Multi-sample Mixing for Contrastive Learning
  Shaofeng Zhang, Meng Liu, Junchi Yan, Hengrui Zhang, Lingxiao Huang, Pinyan Lu, Xiaokang Yang
  KDD'2022 [Paper] [Code]

  M-Mix Framework

  

• A Simple Data Mixing Prior for Improving Self-Supervised Learning
  Sucheng Ren, Huiyu Wang, Zhengqi Gao, Shengfeng He, Alan Yuille, Yuyin Zhou, Cihang Xie
  CVPR'2022 [Paper] [Code]

  SDMP Framework

  

• On the Importance of Asymmetry for Siamese Representation Learning
  Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen
  CVPR'2022 [Paper] [Code]

  ScaleMix Framework

  

• VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
  Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo
  ICML'2022 [Paper]

  VLMixer Framework

  

• CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
  Junlin Han, Lars Petersson, Hongdong Li, Ian Reid
  ArXiv'2022 [Paper] [Code]

  CropMix Framework

  

• i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable
  Kevin Zhang, Zhiqiang Shen
  ArXiv'2022 [Paper] [Code]

  i-MAE Framework

  

• MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers
  Jihao Liu, Xin Huang, Jinliang Zheng, Yu Liu, Hongsheng Li
  CVPR'2023 [Paper] [Code]

  MixMAE Framework

  

• Mixed Autoencoder for Self-supervised Visual Representation Learning
  Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung
  CVPR'2023 [Paper]

  MixedAE Framework

  

• Inter-Instance Similarity Modeling for Contrastive Learning
  Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang
  ArXiv'2023 [Paper] [Code]

  PatchMix Framework

  

• Guarding Barlow Twins Against Overfitting with Mixed Samples
  Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel
  ArXiv'2023 [Paper] [Code]

  PatchMix Framework

  

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Mixup for Semi-supervised Learning

• MixMatch: A Holistic Approach to Semi-Supervised Learning
  David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
  NIPS'2019 [Paper] [Code]

  MixMatch 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

  

• ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
  David Berthelot, [e-mail], Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Kihyuk Sohn, Han Zhang, Colin Raffel
  ICLR'2020 [Paper] [Code]

  ReMixMatch Framework

  

• DivideMix: Learning with Noisy Labels as Semi-supervised Learning
  Junnan Li, Richard Socher, Steven C.H. Hoi
  ICLR'2020 [Paper] [Code]

  DivideMix Framework

  

• Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff
  Vincent Pisztora, Yanglan Ou, Xiaolei Huang, Francesca Chiaromonte, Jia Li
  ArXiv'2021 [Paper]

  Epsilon Consistent Mixup (ϵmu) Framework

  

• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
  Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng
  NIPS'2021 [Paper] [Code]

  Core-Tuning Framework

  

• MUM: Mix Image Tiles and UnMix Feature Tiles for Semi-Supervised Object Detection
  JongMok Kim, Jooyoung Jang, Seunghyeon Seo, Jisoo Jeong, Jongkeun Na, Nojun Kwak
  CVPR'2022 [Paper] [Code]

  MUM Framework

  

• Harnessing Hard Mixed Samples with Decoupled Regularizer
  Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
  NIPS'2023 [Paper] [Code]

  DFixMatch Framework

  

• Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
  Fahimeh Fooladgar, Minh Nguyen Nhat To, Parvin Mousavi, Purang Abolmaesumi
  Arxiv'2023 [Paper] [Code]

  MixEMatch Framework

  

• LaserMix for Semi-Supervised LiDAR Semantic Segmentation
  Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu
  CVPR'2023 [Paper] [Code] [project]

  LaserMix Framework

  

• Dual-Decoder Consistency via Pseudo-Labels Guided Data Augmentation for Semi-Supervised Medical Image Segmentation
  Yuanbin Chen, Tao Wang, Hui Tang, Longxuan Zhao, Ruige Zong, Tao Tan, Xinlin Zhang, Tong Tong
  ArXiv'2023 [Paper]

  DCPA Framework

  

• Mixed Pseudo Labels for Semi-Supervised Object Detection
  Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang
  ArXiv'2023 [Paper] [Code]

  MixPL Framework

  

Mixup for Regression

• RegMix: Data Mixing Augmentation for Regression
  Seong-Hyeon Hwang, Steven Euijong Whang
  ArXiv'2021 [Paper]

• C-Mixup: Improving Generalization in Regression
  Huaxiu Yao, Yiping Wang, Linjun Zhang, James Zou, Chelsea Finn
  NeurIPS'2022 [Paper] [Code]

• ExtraMix: Extrapolatable Data Augmentation for Regression using Generative Models
  Kisoo Kwon, Kuhwan Jeong, Sanghyun Park, Sangha Park, Hoshik Lee, Seung-Yeon Kwak, Sungmin Kim, Kyunghyun Cho
  OpenReview'2022 [Paper]

• Anchor Data Augmentation
  Nora Schneider, Shirin Goshtasbpour, Fernando Perez-Cruz
  NeurIPS'2023 [Paper]

• Rank-N-Contrast: Learning Continuous Representations for Regression
  Kaiwen Zha, Peng Cao, Jeany Son, Yuzhe Yang, Dina Katabi
  NeurIPS'2023 [Paper] [Code]

• Mixup Your Own Pairs
  Yilei Wu, Zijian Dong, Chongyao Chen, Wangchunshu Zhou, Juan Helen Zhou
  ArXiv'2023 [Paper] [Code]

  SupReMix Framework

  

• Tailoring Mixup to Data using Kernel Warping functions
  Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
  ArXiv'2023 [Paper] [Code]

  SupReMix Framework

  

• OmniMixup: Generalize Mixup with Mixing-Pair Sampling Distribution
  Anonymous
  Openreview'2023 [Paper]

• Augment on Manifold: Mixup Regularization with UMAP
  Yousef El-Laham, Elizabeth Fons, Dillon Daudert, Svitlana Vyetrenko
  ICASSP'2024 [Paper]

Mixup for Robustness

• Mixup as directional adversarial training
  Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang
  NeurIPS'2019 [Paper] [Code]

• Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
  Tianyu Pang, Kun Xu, Jun Zhu
  ICLR'2020 [Paper] [Code]

• Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
  Alfred Laugros, Alice Caplier, Matthieu Ospici
  ECCV'2020 [Paper]

• Mixup Training as the Complexity Reduction
  Masanari Kimura
  OpenReview'2021 [Paper]

• Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
  Saehyung Lee, Hyungyu Lee, Sungroh Yoon
  CVPR'2020 [Paper] [Code]

• MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
  Muhammad Awais, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li
  NeurIPS'2021 [Paper]

• On the benefits of defining vicinal distributions in latent space
  Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian
  CVPRW'2021 [Paper]

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Mixup for Multi-modality

• MixGen: A New Multi-Modal Data Augmentation
  Xiaoshuai Hao, Yi Zhu, Srikar Appalaraju, Aston Zhang, Wanqian Zhang, Bo Li, Mu Li
  arXiv'2023 [Paper] [Code]

• VLMixer: Unpaired Vision-Language Pre-training via Cross-Modal CutMix
  Teng Wang, Wenhao Jiang, Zhichao Lu, Feng Zheng, Ran Cheng, Chengguo Yin, Ping Luo
  arXiv'2022 [Paper]

• Geodesic Multi-Modal Mixup for Robust Fine-Tuning
  Changdae Oh, Junhyuk So, Hoyoon Byun, YongTaek Lim, Minchul Shin, Jong-June Jeon, Kyungwoo Song
  NeurIPS'2023 [Paper] [Code]

• PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis
  Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos
  arXiv'2023 [Paper]

  PowMix Framework

  

Analysis of Mixup

• On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
  Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak
  NeurIPS'2019 [Paper] [Code]

  Framework

  

• On Mixup Regularization
  Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert
  ArXiv'2020 [Paper]

  Framework

  

• How Does Mixup Help With Robustness and Generalization?
  Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou
  ICLR'2021 [Paper]

  Framework

  

• Towards Understanding the Data Dependency of Mixup-style Training
  Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge
  ICLR'2022 [Paper] [Code]

  Framework

  

• When and How Mixup Improves Calibration
  Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou
  ICML'2022 [Paper]

  Framework

  

• Over-Training with Mixup May Hurt Generalization
  Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao
  ICLR'2023 [Paper]

  Framework

  

• Provable Benefit of Mixup for Finding Optimal Decision Boundaries
  Junsoo Oh, Chulhee Yun
  ICML'2023 [Paper]

• On the Pitfall of Mixup for Uncertainty Calibration
  Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang
  CVPR'2023 [Paper]

• Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
  Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga
  WACV'2023 [Paper] [Code]

• Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability
  Soyoun Won, Sung-Ho Bae, Seong Tae Kim
  arXiv'2023 [Paper]

• Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup
  Damien Teney, Jindong Wang, Ehsan Abbasnejad
  arXiv'2023 [Paper]

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Natural Language Processing

• Augmenting Data with Mixup for Sentence Classification: An Empirical Study
  Hongyu Guo, Yongyi Mao, Richong Zhang
  arXiv'2019 [Paper] [Code]

• Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks
  Lichao Sun, Congying Xia, Wenpeng Yin, Tingting Liang, Philip S. Yu, Lifang He
  COLING'2020 [Paper]

• Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
  Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, Chao Zhang
  EMNLP'2020 [Paper] [Code]

• Augmenting NLP Models using Latent Feature Interpolations
  Amit Jindal, Arijit Ghosh Chowdhury, Aniket Didolkar, Di Jin, Ramit Sawhney, Rajiv Ratn Shah
  COLING'2020 [Paper]

• MixText: Linguistically-informed Interpolation of Hidden Space for Semi-Supervised Text Classification
  Jiaao Chen, Zichao Yang, Diyi Yang
  ACL'2020 [Paper] [Code]

• TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
  Le Zhang, Zichao Yang, Diyi Yang
  NAALC'2022 [Paper] [Code]

• STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
  Qingkai Fang, Rong Ye, Lei Li, Yang Feng, Mingxuan Wang
  ACL'2022 [Paper] [Code]

• Enhancing Cross-lingual Transfer by Manifold Mixup
  Huiyun Yang, Huadong Chen, Hao Zhou, Lei Li
  ICLR'2022 [Paper] [Code]

Graph Representation Learning

• Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
  Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, Wenwu Zhu
  NeurIPS'2023 [Paper] [code]

• G-Mixup: Graph Data Augmentation for Graph Classification
  Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
  NeurIPS'2023 [Paper]

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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]

• 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]

• A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate
  Tsz-Him Cheung, Dit-Yan Yeung
  TNNLS'2023 [Paper]

• Survey: Image Mixing and Deleting for Data Augmentation
  Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, Ajmal Mian
  Engineering Applications of Artificial Intelligence'2024 [Paper]

Benchmark

• OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification
  Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Weiyang Jin, Stan Z. Li
  ArXiv'2022 [Paper] [Code]

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Contribution

Feel free to send pull requests to add more links with the following Markdown format. Note that the abbreviation, the code link, and the figure link are optional attributes.

* **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>

Current contributors include: Siyuan Li (@Lupin1998), Zicheng Liu (@pone7), and Zedong Wang (@Jacky1128). We thank all contributors for Awesome-Mixup!

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License

This project is released under the Apache 2.0 license.

Acknowledgement

This repository is built using the OpenMixup library and Awesome README repository.

Related Project

• OpenMixup: CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark.
• Awesome-Mix: An awesome list of papers for A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability, we categorize them based on our proposed taxonomy.
• survery-image-mixing-and-deleting-for-data-augmentation: An awesome list of papers for Survey: Image Mixing and Deleting for Data Augmentation.
• awesome-mixup: A collection of awesome papers about mixup.
• awesome-mixed-sample-data-augmentation: A collection of awesome things about mixed sample data augmentation.
• data-augmentation-review: List of useful data augmentation resources.