Awesome-Mixup

Welcome to Awesome-Mixup, a carefully curated survey of Mixup algorithms implemented in the PyTorch library, aiming to meet various needs of the research community. Mixup is a kind of methods that focus on alleviating model overfitting and poor generalization. As a "data-centric" way, Mixup can be applied to various training paradigms and data modalities.

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Introduction

We summarize awesome mixup data augmentation methods for visual representation learning in various scenarios from 2018 to 2024.

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. You can read our survey: A Survey on Mixup Augmentations and Beyond see more detailed information.

• 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.

Figuer of Contents

You can see the figuer of mixup augmentation methods deirtly that we summarized.

Table of Contents

• Awesome-Mixup
  • Introduction
  • Table of Contents
  • Sample Mixup Policies in SL
    • Static Linear
    • Feature-based
    • Cutting-based
    • K Samples Mixup
    • Random Policies
    • Style-based
    • Saliency-based
    • Attention-based
    • Generating Samples
  • Label Mixup Policies in SL
    • Optimizing Calibration
    • Area-based
    • Loss Object
    • Random Label Policies
    • Optimizing Mixing Ratio
    • Generating Label
    • Attention Score
    • Saliency Token
  • Self-Supervised Learning
    • Contrastive Learning
    • Masked Image Modeling
  • Semi-Supervised Learning
  • CV Downstream Tasks
    • Regression
    • Long tail distribution
    • Segmentation
    • Object Detection
  • Training Paradigms
    • Federated Learning
    • Adversarial Attack and Adversarial Training
    • Domain Adaption
    • Knowledge Distillation
    • Multi-Modal
  • Beyond Vision
    • NLP
    • GNN
    • 3D Point
    • Other
  • Analysis and Theorem
  • Survey
  • Benchmark
  • Classification Results on Datasets
  • Related Datasets Link
  • Contribution
  • License
  • Acknowledgement
  • Related Project

Sample Mixup Policies in SL

Static Linear

• 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

  

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

  LocalMixup 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
  CVPR'2022 [Paper] [Code]

  PixMix Framework

  

• IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers
  Zhenglin Huang, Xiaoan Bao, Na Zhang, Qingqi Zhang, Xiaomei Tu, Biao Wu, Xi Yang
  NIPS'2023 [Paper] [Code]

  IPMix Framework

  

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Feature-based

• 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

  

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

  PatchUp 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

  

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

  Catch-Up-Mix Framework

  

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Cutting-based

• 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

  

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

  MixedExamples 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

  

• FMix: Enhancing Mixed Sample Data Augmentation
  Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare
  arXiv'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

  

• 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
  arXiv'2020 [Paper] [Code]

  ResizeMix Framework

  

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

  StackMix 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

  

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

  MSDA Framework

  

• You Only Cut Once: Boosting Data Augmentation with a Single Cut
  Junlin Han, Pengfei Fang, Weihao Li, Jie Hong, Mohammad Ali Armin, Ian Reid, Lars Petersson, Hongdong Li
  ICML'2022 [Paper] [Code]

  YOCO Framework

  

• StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification
  Xin Jin, Hongyu Zhu, Mounîm A.El Yacoubi, Hongchao Liao, Huafeng Qin, Yun Jiang
  arXiv'2024 [Paper]

  StarMix Framework

  

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K Samples Mixup

• You Only Look Once: Unified, Real-Time Object Detection
  Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi
  CVPR'2016 [Paper] [Code]

  Mosaic

  

• Data Augmentation using Random Image Cropping and Patching for Deep CNNs
  Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
  IEEE TCSVT'2020 [Paper]

  RICAP

  

• 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

  

• 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

  

• MixMo: Mixing Multiple Inputs for Multiple Outputs via Deep Subnetworks
  Alexandre Rame, Remy Sun, Matthieu Cord
  ICCV'2021 [Paper]

  MixMo Framework

  

• Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
  Tianshu Xie, Xuan Cheng, Minghui Liu, Jiali Deng, Xiaomin Wang, Ming Liu
  ACM MM;2021 [Paper]

  Cut-Thumbnail

  

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Random Policies

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

  RandomMix 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

  

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Style-based

• 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

  

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

  AlignMixup Framework

  

• Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
  Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
  NIPS'2023 [Paper]
  MultiMix Framework

  

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Saliency-based

• 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

  

• 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

  

• 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

  

• 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

  

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

  RecursiveMix Framework

  

• TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation
  Tsz-Him Cheung, Dit-Yan Yeung
  OpenReview'2023 [Paper]

  TransformMix Framework

  

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

  GuidedMixup 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

  

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

  AdAutoMix Framework

  

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Attention-based

• 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

  

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

  TokenMixup Framework

  

• ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
  Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran
  WACV'2023 [Paper]

  ScoreMix 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

  

• 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

  

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Generating Samples

• Data Augmentation via Latent Space Interpolation for Image Classification
  *Xiaofeng Liu, Yang Zou, Lingsheng Kong, Zhihui Diao, Junliang Yan, Jun Wang, Site Li, Ping Jia, Jane You
  ICPR'2018 [Paper]

  AEE 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

  

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

  AutoMix Framework

  

• VarMixup: Exploiting the Latent Space for Robust Training and Inference
  Puneet Mangla, Vedant Singh, Shreyas Jayant Havaldar, Vineeth N Balasubramanian
  CVPRW'2021 [Paper]

  VarMixup Framework

  

• DiffuseMix: Label-Preserving Data Augmentation with Diffusion Models
  Khawar Islam, Muhammad Zaigham Zaheer, Arif Mahmood, Karthik Nandakumar
  CVPR'2024 [Paper] [Code]

  DiffuseMix Framework

  

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Label Mixup Policies in SL

Optimizing Calibration

• 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]

  CAMix Framework

  

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

  RankMixup Framework

  

• SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness
  Jongheon Jeong, Sejun Park, Minkyu Kim, Heung-Chang Lee, Doguk Kim, Jinwoo Shin
  NIPS'2021 [Paper] [Code]

  SmoothMixup Framework

  

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Area-based

• 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

  

• Data Augmentation using Random Image Cropping and Patching for Deep CNNs
  Ryo Takahashi, Takashi Matsubara, Kuniaki Uehara
  IEEE TCSVT'2020 [Paper]

  RICAP

  

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

  RecursiveMix Framework

  

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Loss Object

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

  DecoupledMix 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

  

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Random Label Policies

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

• 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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Optimizing Mixing Ratio

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

  AdaMixup 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

  

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

  MetaMixup Framework

  

• LUMix: Improving Mixup by Better Modelling Label Uncertainty
  Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille, Philip Torr, Song Bai
  ICASSP'2024 [Paper] [Code]

  LUMix Framework

  

• SUMix: Mixup with Semantic and Uncertain Information
  Huafeng Qin, Xin Jin, Hongyu Zhu, Hongchao Liao, Mounîm A. El-Yacoubi, Xinbo Gao
  ECCV'2024 [Paper] [Code]

  SUMix Framework

  

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Generating Label

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

  

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Attention Score

• All Tokens Matter: Token Labeling for Training Better Vision Transformers
  Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng
  NIPS'2021 [Paper] [Code]

  Token Labeling 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

  

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

  TokenMixup 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

  

• Token-Label Alignment for Vision Transformers
  Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
  ICCV'2023 [Paper] [Code]

  TL-Align Framework

  

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Saliency Token

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

  SnapMix 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

  

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Self-Supervised Learning

Contrastive 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

  

• 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

  

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

  MixSSL 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]

  Co-Tuning Framework

  

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

  CLIM 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

  

• 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

  

• 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

  

• 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

  

• 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

  

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

  CropMix Framework

  

• Mixing up contrastive learning: Self-supervised representation learning for time series
  Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen
  PR Letter'2022 [Paper]

  MCL Framework

  

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

  DACL 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

  

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

  PatchMix 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

  

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

  m2-Mix Framework

  

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Masked Image Modeling

• 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

  

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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

  

• ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
  David Berthelot, 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

  

• MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning
  Tong Wei, Feng Shi, Hai Wang, Wei-Wei Tu. Yu-Feng Li
  arXiv'2020 [Paper]

  MixPUL Framework

• Milking CowMask for Semi-Supervised Image Classification
  Geoff French, Avital Oliver, Tim Salimans
  NIPS'2020 [Paper] [Code]

  CowMask 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

  

• Who Is Your Right Mixup Partner in Positive and Unlabeled Learning
  Changchun Li, Ximing Li, Lei Feng, Jihong Ouyang
  ICLR'2021 [Paper]

  P3Mix Framework

  

• Interpolation Consistency Training for Semi-Supervised Learning
  Vikas Verma, Kenji Kawaguchi, Alex Lamb, Juho Kannala, Arno Solin, Yoshua Bengio, David Lopez-Paz
  NN'2022 [Paper]

  ICT 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

  

• 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

  

• PCLMix: Weakly Supervised Medical Image Segmentation via Pixel-Level Contrastive Learning and Dynamic Mix Augmentation
  Yu Lei, Haolun Luo, Lituan Wang, Zhenwei Zhang, Lei Zhang
  arXiv'2024 [Paper] [Code]

  PCLMix Framework

  

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CV Downstream Tasks

Regression

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

  MixRL Framework

  

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

  C-Mixup Framework

  

• 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]

  SupReMix Framework

  

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

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

• 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]

  Warped Mixup 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]

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Long tail distribution

• Remix: Rebalanced Mixup
  Hsin-Ping Chou, Shih-Chieh Chang, Jia-Yu Pan, Wei Wei, Da-Cheng Juan
  ECCVW'2020 [Paper]

  Remix Framework

  

• Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
  Zhengzhuo Xu, Zenghao Chai, Chun Yuan
  NIPS'2021 [Paper] [Code]

  UniMix Framework

  

• Label-Occurrence-Balanced Mixup for Long-tailed Recognition
  Shaoyu Zhang, Chen Chen, Xiujuan Zhang, Silong Peng
  ICASSP'2022 [Paper]

  OBMix Framework

  

• DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition
  Jae Soon Baik, In Young Yoon, Jun Won Choi
  PR'2024 [Paper]

  DBN-Mix Framework

  

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Segmentation

• ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
  Viktor Olsson, Wilhelm Tranheden, Juliano Pinto, Lennart Svensson
  WACV'2021 [Paper] [Code]

  ClassMix Framework

  

• ChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation
  Matheus Barros Pereira, Jefersson Alex dos Santos
  SIBGRAPI'2021 [Paper]

  ChessMix Framework

  

• CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
  Ke Zhang, Xiahai Zhuang
  CVPR'2022 [Paper] [Code]

  CyclesMix Framework

  

• InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation
  Yi Lin, Zeyu Wang, Kwang-Ting Cheng, Hao Chen
  MICCAI'2022 [Paper] [Code]

  InsMix 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

  

• SA-MixNet: Structure-aware Mixup and Invariance Learning for Scribble-supervised Road Extraction in Remote Sensing Images
  Jie Feng, Hao Huang, Junpeng Zhang, Weisheng Dong, Dingwen Zhang, Licheng Jiao
  arXiv'2024 [Paper] [Code]

  SA-MixNet Framework

  

• Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation
  Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu, Yinghuan Shi, Yang Gao
  CVPR'2024 [Paper] [Code]

  MiDSS Framework

  

• UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weather
  Haimei Zhao, Jing Zhang, Zhuo Chen, Shanshan Zhao, Dacheng Tao
  CVPR'2024 [Paper] [Code]

• ModelMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision
  Ke Zhang, Vishal M. Patel
  MICCAI'2024 [Paper]

  ModelMix Framework

  

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Object Detection

• 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

  

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

  MixPL Framework

  

• MS-DETR: Efficient DETR Training with Mixed Supervision
  Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, Jingdong Wang
  arXiv'2024 [Paper] [Code]

  MS-DETR Framework

  

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Other Applications

Training Paradigms

Federated Learning

• XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
  MyungJae Shin, Chihoon Hwang, Joongheon Kim, Jihong Park, Mehdi Bennis, Seong-Lyun Kim
  ICML'2020 [Paper] [Code]

• FedMix: Approximation of Mixup Under Mean augmented Federated Learning
  Tehrim Yoon, Sumin Shin, Sung Ju Hwang, Eunho Yang
  ECCV'2022 [Paper] [Code]

• Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
  Seungeun Oh, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim
  IEEE Communications Letters'2020 [Paper]

• StatMix: Data augmentation method that relies on image statistics in federated learning
  Dominik Lewy, Jacek Mańdziuk, Maria Ganzha, Marcin Paprzycki
  ICONIP'2022 [Paper]

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Adversarial Attack and Adversarial Training

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

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

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

• Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup
  Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li
  EMNLP'2021 [Paper]

• Adversarially Optimized Mixup for Robust Classification
  Jason Bunk, Srinjoy Chattopadhyay, B. S. Manjunath, Shivkumar Chandrasekaran
  arXiv'2021 [Paper]

• Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning
  Guillaume P. Archambault, Yongyi Mao, Hongyu Guo, Richong Zhang
  ACL'2021 [Paper]

• Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
  Alex Lamb, Vikas Verma, Kenji Kawaguchi, Alexander Matyasko, Savya Khosla, Juho Kannala, Yoshua Bengio
  NN'2021 [Paper]

• Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
  Ruochen Jiao, Xiangguo Liu, Takami Sato, Qi Alfred Chen, Qi Zhu
  ICCV'2023 [Paper]

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

• 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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Domain Adaption

• Mix-up Self-Supervised Learning for Contrast-agnostic Applications
  Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann
  ICDE'2022 [Paper]

• 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]

• Virtual Mixup Training for Unsupervised Domain Adaptation
  Xudong Mao, Yun Ma, Zhenguo Yang, Yangbin Chen, Qing Li
  arXiv'2019 [Paper] [Code]

• Improve Unsupervised Domain Adaptation with Mixup Training
  Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren
  arXiv'2020 [Paper]

• Adversarial Domain Adaptation with Domain Mixup
  Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang
  AAAI'2020 [Paper] [Code]

• Dual Mixup Regularized Learning for Adversarial Domain Adaptation
  Yuan Wu, Diana Inkpen, Ahmed El-Roby
  ECCV'2020 [Paper] [Code]

• Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation
  Aadarsh Sahoo, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das
  WACV'2023 [Paper] [Code]

• Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
  Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
  MICCAI'2023 [Paper] [Code]

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Knowledge Distillation

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

• MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
  Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang
  ECCV'2022 [Paper]

• Understanding the Role of Mixup in Knowledge Distillation: An Empirical Study
  Chuanguang Yang, Zhulin An, Helong Zhou, Linhang Cai, Xiang Zhi, Jiwen Wu, Yongjun Xu, Qian Zhang
  WACV'2023 [Paper]

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Multi-Modal

• 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
  ICML'2022 [Paper]

  VLMixer Framework

  

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

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

  PowMix Framework

  

• Enhance image classification via inter-class image mixup with diffusion model
  Efthymios Georgiou, Yannis Avrithis, Alexandros Potamianos
  CVPR'2024 [Paper] [Code]

• Frequency-Enhanced Data Augmentation for Vision-and-Language Navigation
  Keji He, Chenyang Si, Zhihe Lu, Yan Huang, Liang Wang, Xinchao Wang
  NIPS'2023 [Paper] [Code]

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Beyond Vision

NLP

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

• Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup
  Guang Liu, Yuzhao Mao, Hailong Huang, Weiguo Gao, Xuan Li
  EMNLP'2021 [Paper]

• SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
  Hongyu Guo, Yongyi Mao, Richong Zhang
  EMNLP'2020 [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]

• Sequence-Level Mixed Sample Data Augmentation
  Jiaao Chen, Zichao Yang, Diyi Yang
  EMNLP'2020 [Paper] [Code]

• AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
  Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein
  ACL'2020 [Paper] [Code]

• Local Additivity Based Data Augmentation for Semi-supervised NER
  Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang
  ACL'2020 [Paper] [Code]

• Mixup Decoding for Diverse Machine Translation
  Jicheng Li, Pengzhi Gao, Xuanfu Wu, Yang Feng, Zhongjun He, Hua Wu, Haifeng Wang
  EMNLP'2021 [Paper]

• 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]

• AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation
  Chang Jin, Shigui Qiu, Nini Xiao, Hao Jia
  IJCAI'2022 [Paper]

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

• Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translation
  Yong Cheng, Ankur Bapna, Orhan Firat, Yuan Cao, Pidong Wang, Wolfgang Macherey
  ACL'2022 [Paper]

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GNN

• Node Augmentation Methods for Graph Neural Network based Object Classification
  Yifan Xue; Yixuan Liao; Xiaoxin Chen; Jingwei Zhao
  CDS'2021 [Paper]

• Mixup for Node and Graph Classification
  Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi
  WWW'2021 [Paper] [Code]

• Graph Mixed Random Network Based on PageRank
  Qianli Ma, Zheng Fan, Chenzhi Wang, Hongye Tan
  Symmetry'2022 [Paper]

• GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks
  Tianxiang Zhao, Xiang Zhang, Suhang Wang
  WSDM'2021 [Paper]

• GraphMix: Improved Training of GNNs for Semi-Supervised Learning
  Vikas Verma, Meng Qu, Kenji Kawaguchi, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang
  AAAI'2021 [Paper] [Code]

• GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
  Lirong Wu, Haitao Lin, Zhangyang Gao, Cheng Tan, Stan.Z.Li
  ECML-PKDD'2022 [Paper] [Code]

• Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation
  Joonhyung Park, Hajin Shim, Eunho Yang
  AAAI'2022 [Paper] [Code]

• G-Mixup: Graph Data Augmentation for Graph Classification
  Xiaotian Han, Zhimeng Jiang, Ninghao Liu, Xia Hu
  ICML'2022 [Paper]

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

• iGraphMix: Input Graph Mixup Method for Node Classification
  Jongwon Jeong, Hoyeop Lee, Hyui Geon Yoon, Beomyoung Lee, Junhee Heo, Geonsoo Kim, Kim Jin Seon
  ICLR'2024 [Paper]

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3D Point

• PointMixup: Augmentation for Point Clouds
  Yunlu Chen, Vincent Tao Hu, Efstratios Gavves, Thomas Mensink, Pascal Mettes, Pengwan Yang, Cees G.M. Snoek
  ECCV'2020 [Paper] [Code]

• PointCutMix: Regularization Strategy for Point Cloud Classification
  Jinlai Zhang, Lyujie Chen, Bo Ouyang, Binbin Liu, Jihong Zhu, Yujing Chen, Yanmei Meng, Danfeng Wu
  Neurocomputing'2022 [Paper] [Code]

• Regularization Strategy for Point Cloud via Rigidly Mixed Sample
  Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee
  CVPR'2021 [Paper] [Code]

• Part-Aware Data Augmentation for 3D Object Detection in Point Cloud
  Jaeseok Choi, Yeji Song, Nojun Kwak
  IROS'2021 [Paper] [Code]

• Point MixSwap: Attentional Point Cloud Mixing via Swapping Matched Structural Divisions
  Ardian Umam, Cheng-Kun Yang, Yung-Yu Chuang, Jen-Hui Chuang, Yen-Yu Lin
  ECCV'2022 [Paper] [Code]

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Other

• Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
  Byungsoo Ko, Geonmo Gu
  CVPR'2020 [Paper] [Code]

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

• Octave Mix: Data Augmentation Using Frequency Decomposition for Activity Recognition
  Tatsuhito Hasegawa
  IEEE Access'2021 [Paper]

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

• Recall@k Surrogate Loss with Large Batches and Similarity Mixup
  Yash Patel, Giorgos Tolias, Jiri Matas
  CVPR'2022 [Paper] [Code]

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

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

• It Takes Two to Tango: Mixup for Deep Metric Learning
  Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis
  ICLR'2022 [Paper] [Code]

• Representational Continuity for Unsupervised Continual Learning
  Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang
  ICLR'2022 [Paper] [Code]

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

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

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

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

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

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

• Single-channel speech enhancement using learnable loss mixup
  Oscar Chang, Dung N. Tran, Kazuhito Koishida
  arXiv'2023 [Paper]

• Selective Volume Mixup for Video Action Recognition
  Yi Tan, Zhaofan Qiu, Yanbin Hao, Ting Yao, Xiangnan He, Tao Mei
  arXiv'2023 [Paper]

• Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
  Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
  CVPR'2020 & IJCV'2024 [Paper] [Code]

• DNABERT-S: Learning Species-Aware DNA Embedding with Genome Foundation Models
  Zhihan Zhou, Weimin Wu, Harrison Ho, Jiayi Wang, Lizhen Shi, Ramana V Davuluri, Zhong Wang, Han Liu
  arXiv'2024 [Paper] [Code]

• 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]

• Robust Image Denoising through Adversarial Frequency Mixup
  Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, Bohyung Han
  CVPR'2024 [Paper] [Code]

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Analysis and Theorem

• Understanding Mixup Training Methods
  Daojun Liang, Feng Yang, Tian Zhang, Peter Yang
  NIPS'2019 [Paper]

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

• MixUp as Directional Adversarial Training
  Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
  NIPS'2019 [Paper]

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

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

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

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

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

• 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]

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

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

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

• 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]

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

• 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
  ICML'2024 [Paper]

• Pushing Boundaries: Mixup's Influence on Neural Collapse
  Quinn Fisher, Haoming Meng, Vardan Papyan
  ICLR'2024 [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
  IEEE TNNLS'2023 [Paper]

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

• A Survey on Mixup Augmentations and Beyond
  Xin Jin, Hongyu Zhu, Siyuan Li, Zedong Wang, Zecheng Liu, Chang Yu, Huafeng Qin, Stan. Z. Li
  arXiv'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'2024 [Paper] [Code]

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Classification Results on Datasets

Mixup methods classification results on general datasets: CIFAR10 \ CIFAR100, TinyImageNet, and ImageNet-1K. $(\cdot)$ denotes training epochs based on ResNet18 (R18), ResNet50 (R50), ResNeXt50 (RX50), PreActResNet18 (PreActR18), and Wide-ResNet28 (WRN28-10, WRN28-8).

Method	Publish	CIFAR10	CIFAR100	CIFAR100	CIFAR100	CIFAR100	CIFAR100	Tiny-ImageNet	Tiny-ImageNet	ImageNet-1K	ImageNet-1K
		R18	R18	RX50	PreActR18	WRN28-10	WRN28-8	R18	RX50	R18	R50
MixUp	ICLR'2018	96.62(800)	79.12(800)	82.10(800)	78.90(200)	82.50(200)	82.82(400)	63.86(400)	66.36(400)	69.98(100)	77.12(100)
CutMix	ICCV'2019	96.68(800)	78.17(800)	78.32(800)	76.80(1200)	83.40(200)	84.45(400)	65.53(400)	66.47(400)	68.95(100)	77.17(100)
Manifold Mixup	ICML'2019	96.71(800)	80.35(800)	82.88(800)	79.66(1200)	81.96(1200)	83.24(400)	64.15(400)	67.30(400)	69.98(100)	77.01(100)
FMix	arXiv'2020	96.18(800)	79.69(800)	79.02(800)	79.85(200)	82.03(200)	84.21(400)	63.47(400)	65.08(400)	69.96(100)	77.19(100)
SmoothMix	CVPRW'2020	96.17(800)	78.69(800)	78.95(800)	-	-	82.09(400)	-	-	-	77.66(300)
GridMix	PR'2020	96.56(800)	78.72(800)	78.90(800)	-	-	84.24(400)	64.79(400)	-	-	-
ResizeMix	arXiv'2020	96.76(800)	80.01(800)	80.35(800)	-	85.23(200)	84.87(400)	63.47(400)	65.87(400)	69.50(100)	77.42(100)
SaliencyMix	ICLR'2021	96.20(800)	79.12(800)	78.77(800)	80.31(300)	83.44(200)	84.35(400)	64.60(400)	66.55(400)	69.16(100)	77.14(100)
Attentive-CutMix	ICASSP'2020	96.63(800)n	78.91(800)	80.54(800)	-	-	84.34(400)	64.01(400)	66.84(400)	-	77.46(100)
Saliency Grafting	AAAI'2022	-	80.83(800)	83.10(800)	-	84.68(300)	-	64.84(600)	67.83(400)	-	77.65(100)
PuzzleMix	ICML'2020	97.10(800)	81.13(800)	82.85(800)	80.38(1200)	84.05(200)	85.02(400)	65.81(400)	67.83(400)	70.12(100)	77.54(100)
Co-Mix	ICLR'2021	97.15(800)	81.17(800)	82.91(800)	80.13(300)	-	85.05(400)	65.92(400)	68.02(400)	-	77.61(100)
SuperMix	CVPR'2021	-	-	-	79.07(2000)	93.60(600)	-	-	-	-	77.60(600)
RecursiveMix	NIPS'2022	-	81.36(200)	-	80.58(2000)	-	-	-	-	-	79.20(300)
AutoMix	ECCV'2022	97.34(800)	82.04(800)	83.64(800)	-	-	85.18(400)	67.33(400)	70.72(400)	70.50(100)	77.91(100)
SAMix	arXiv'2021	97.50(800)	82.30(800)	84.42(800)	-	-	85.50(400)	68.89(400)	72.18(400)	70.83(100)	78.06(100)
AlignMixup	CVPR'2022	-	-	-	81.71(2000)	-	-	-	-	-	78.00(100)
MultiMix	NIPS'2023	-	-	-	81.82(2000)	-	-	-	-	-	78.81(300)
GuidedMixup	AAAI'2023	-	-	-	81.20(300)	84.02(200)	-	-	-	-	77.53(100)
Catch-up Mix	AAAI'2023	-	82.10(400)	83.56(400)	82.24(2000)	-	-	68.84(400)	-	-	78.71(300)
LGCOAMix	TIP'2024	-	82.34(800)	84.11(800)	-	-	-	68.27(400)	73.08(400)	-	-
AdAutoMix	ICLR'2024	97.55(800)	82.32(800)	84.42(800)	-	-	85.32(400)	69.19(400)	72.89(400)	70.86(100)	78.04(100)

Mixup methods classification results on ImageNet-1K dataset use ViT-based models: DeiT, Swin Transformer (Swin), Pyramid Vision Transformer (PVT), and ConvNext trained 300 epochs.

Method	Publish	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K	ImageNet-1K
		DieT-Tiny	DieT-Small	DieT-Base	Swin-Tiny	PVT-Tiny	PVT-Small	ConvNeXt-Tiny
MixUp	ICLR'2018	74.69	77.72	78.98	81.01	75.24	78.69	80.88
CutMix	ICCV'2019	74.23	80.13	81.61	81.23	75.53	79.64	81.57
FMix	arXiv'2020	74.41	77.37	-	79.60	75.28	78.72	81.04
ResizeMix	arXiv'2020	74.79	78.61	80.89	81.36	76.05	79.55	81.64
SaliencyMix	ICLR'2021	74.17	79.88	80.72	81.37	75.71	79.69	81.33
Attentive-CutMix	ICASSP'2020	74.07	80.32	82.42	81.29	74.98	79.84	81.14
PuzzleMix	ICML'2020	73.85	80.45	81.63	81.47	75.48	79.70	81.48
AutoMix	ECCV'2022	75.52	80.78	82.18	81.80	76.38	80.64	82.28
SAMix	arXiv'2021	75.83	80.94	82.85	81.87	76.60	80.78	82.35
TransMix	CVPR'2022	74.56	80.68	82.51	81.80	75.50	80.50	-
TokenMix	ECCV'2022	75.31	80.80	82.90	81.60	75.60	-	73.97
TL-Align	ICCV'2023	73.20	80.60	82.30	81.40	75.50	80.40	-
SMMix	ICCV'2023	75.56	81.10	82.90	81.80	75.60	81.03	-
Mixpro	ICLR'2023	73.80	81.30	82.90	82.80	76.70	81.20	-
LUMix	ICASSP'2024	-	80.60	80.20	81.70	-	-	82.50

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Related Datasets Link

Summary of datasets for mixup methods tasks. Link to dataset websites is provided.

Dataset	Type	Label	Task	Total data number	Link
MINIST	Image	10	Classification	70,000	MINIST
Fashion-MNIST	Image	10	Classification	70,000	Fashion-MINIST
CIFAR10	Image	10	Classification	60,000	CIFAR10
CIFAR100	Image	100	Classification	60,000	CIFAR100
SVHN	Image	10	Classification	630,420	SVHN
GTSRB	Image	43	Classification	51,839	GTSRB
STL10	Image	10	Classification	113,000	STL10
Tiny-ImageNet	Image	200	Classification	100,000	Tiny-ImageNet
ImageNet-1K	Image	1,000	Classification	1,431,167	ImageNet-1K
CUB-200-2011	Image	200	Classification, Object Detection	11,788	CUB-200-2011
FGVC-Aircraft	Image	102	Classification	10,200	FGVC-Aircraft
StanfordCars	Image	196	Classification	16,185	StanfordCars
Oxford Flowers	Image	102	Classification	8,189	Oxford Flowers
Caltech101	Image	101	Classification	9,000	Caltech101
SOP	Image	22,634	Classification	120,053	SOP
Food-101	Image	101	Classification	101,000	Food-101
SUN397	Image	899	Classification	130,519	SUN397
iNaturalist	Image	5,089	Classification	675,170	iNaturalist
CIFAR-C	Image	10,100	Corruption Classification	60,000	CIFAR-C
CIFAR-LT	Image	10,100	Long-tail Classification	60,000	CIFAR-LT
ImageNet-1K-C	Image	1,000	Corruption Classification	1,431,167	ImageNet-1K-C
ImageNet-A	Image	200	Classification	7,500	ImageNet-A
Pascal VOC 102	Image	20	Object Detection	33,043	Pascal VOC 102
MS-COCO Detection	Image	91	Object Detection	164,062	MS-COCO Detection
DSprites	Image	737,280*6	Disentanglement	737,280	DSprites
Place205	Image	205	Recognition	2,500,000	Place205
Pascal Context	Image	459	Segmentation	10,103	Pascal Context
ADE20K	Image	3,169	Segmentation	25,210	ADE20K
Cityscapes	Image	19	Segmentation	5,000	Cityscapes
StreetHazards	Image	12	Segmentation	7,656	StreetHazards
PACS	Image	7*4	Domain Classification	9,991	PACS
BRACS	Medical Image	7	Classification	4,539	BRACS
BACH	Medical Image	4	Classification	400	BACH
CAME-Lyon16	Medical Image	2	Anomaly Detection	360	CAME-Lyon16
Chest X-Ray	Medical Image	2	Anomaly Detection	5,856	Chest X-Ray
BCCD	Medical Image	4,888	Object Detection	364	BCCD
TJU600	Palm-Vein Image	600	Classification	12,000	TJU600
VERA220	Palm-Vein Image	220	Classification	2,200	VERA220
CoNLL2003	Text	4	Classification	2,302	CoNLL2003
20 Newsgroups	Text	20	OOD Detection	20,000	20 Newsgroups
WOS	Text	134	OOD Detection	46,985	WOS
SST-2	Text	2	Sentiment Understanding	68,800	SST-2
Cora	Graph	7	Node Classification	2,708	Cora
Citeseer	Graph	6	Node Classification	3,312	Citeseer
PubMed	Graph	3	Node Classification	19,717	PubMed
BlogCatalog	Graph	39	Node Classification	10,312	BlogCatalog
Google Commands	Speech	30	Classification	65,000	Google Commands
VoxCeleb2	Speech	6,112	Sound Classification	1,000,000+	VoxCeleb2
VCTK	Speech	110	Enhancement	44,000	VCTK
ModelNet40	3D Point Cloud	40	Classification	12,311	ModelNet40
ScanObjectNN	3D Point Cloud	15	Classification	15,000	ScanObjectNN
ShapeNet	3D Point Cloud	16	Recognition, Classification	16,880	ShapeNet
KITTI360	3D Point Cloud	80,256	Detection, Segmentation	14,999	KITTI360
UCF101	Video	101	Action Recognition	13,320	UCF101
Kinetics400	Video	400	Action Recognition	260,000	Kinetics400
Airfoil	Tabular	-	Regression	1,503	Airfoil
NO2	Tabular	-	Regression	500	NO2
Exchange-Rate	Timeseries	-	Regression	7,409	Exchange-Rate
Electricity	Timeseries	-	Regression	26,113	Electricity

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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>

Citation

If you feel that our work has contributed to your research, please cite it, thanks. 🥰

@article{jin2024survey,
  title={A Survey on Mixup Augmentations and Beyond},
  author={Jin, Xin and Zhu, Hongyu and Li, Siyuan and Wang, Zedong and Liu, Zicheng and Yu, Chang and Qin, Huafeng and Li, Stan Z},
  journal={arXiv preprint arXiv:2409.05202},
  year={2024}
}

Current contributors include: Siyuan Li (@Lupin1998), Xin Jin (@JinXins), 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.
• Awesome-Mixup: An awesome list of papers for A Survey on Mixup Augmentations and Beyond.