Awesome Mixup Methods for Self- and Semi-supervised Learning

We summarize mixup methods proposed for self- and semi-supervised visual representation learning. We are working on a survey of mixup methods. The list is on updating.

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Table of Contents

• Mixup for Self-supervised Learning

• Mixup for Semi-supervised Learning

• Contribution

• Related Project

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

  

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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, dberth@google.com, 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

  

• 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

  

• Decoupled Mixup for Data-efficient Learning
  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

  

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Contribution

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

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

Related Project

• Awesome-Mixup: Awesome List of Mixup Augmentation Papers for Visual Representation Learning.

• 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.