Awesome Mixup Methods for Supervised Learning¶
We summarize fundamental mixup methods proposed for supervised visual representation learning from two aspects: sample mixup policy and label mixup policy. Then, we summarize mixup techniques used in downstream tasks. The list of awesome mixup methods is summarized in chronological order and is on updating. And we will add more papers according to Awesome-Mix.
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Table of Contents¶
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
Saliency-guided Policies¶
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
A F M Shahab Uddin and Mst. Sirazam Monira and Wheemyung Shin and TaeChoong Chung and Sung-Ho Bae
ICLR’2021 [Paper] [Code]SaliencyMix Framework
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification
Devesh Walawalkar, Zhiqiang Shen, Zechun Liu, Marios Savvides
ICASSP’2020 [Paper] [Code]AttentiveMix Framework
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Shaoli Huang, Xinchao Wang, Dacheng Tao
AAAI’2021 [Paper] [Code]SnapMix Framework
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
Hao Li, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
VCIP’2020 [Paper]AttributeMix Framework
On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio, Christopher Pal
NIPS’2019 [Paper] [Code]AMR Framework
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
ArXiv’2019 [Paper]Pani VAT Framework
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
Jianchao Zhu, Liangliang Shi, Junchi Yan, Hongyuan Zha
ECCV’2020 [Paper]AutoMix Framework
PuzzleMix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
ICML’2020 [Paper] [Code]PuzzleMix Framework
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
ICLR’2021 [Paper] [Code]Co-Mixup Framework
SuperMix: Supervising the Mixing Data Augmentation
Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi
CVPR’2021 [Paper] [Code]SuperMix Framework
Evolving Image Compositions for Feature Representation Learning
Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez
BMVC’2021 [Paper]PatchMix Framework
StackMix: A complementary Mix algorithm
John Chen, Samarth Sinha, Anastasios Kyrillidis
UAI’2022 [Paper]StackMix Framework
SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, Heechul Jung
Sensor’2021 [Paper]SalfMix Framework
k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
ArXiv’2021 [Paper]k-Mixup Framework
AlignMix: Improving representation by interpolating aligned features
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
CVPR’2022 [Paper] [Code]AlignMix Framework
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
Zicheng Liu, Siyuan Li, Di Wu, Zihan Liu, Zhiyuan Chen, Lirong Wu, Stan Z. Li
ECCV’2022 [Paper] [Code]AutoMix Framework
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li
Arxiv’2021 [Paper] [Code]SAMix Framework
ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification
Thomas Stegmüller, Behzad Bozorgtabar, Antoine Spahr, Jean-Philippe Thiran
Arxiv’2022 [Paper]ScoreMix Framework
RecursiveMix: Mixed Learning with History
Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang
NIPS’2022 [Paper] [Code]RecursiveMix Framework
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Remy Sun, Clement Masson, Gilles Henaff, Nicolas Thome, Matthieu Cord.
ICPR’2022 [Paper]SciMix Framework
TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation
Tsz-Him Cheung, Dit-Yan Yeung.<\br> OpenReview’2023 [Paper]TransformMix Framework
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
Minsoo Kang, Suhyun Kim
AAAI’2023 [Paper]GuidedMixup Framework
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu
ICLR’2023 [Paper]MixPro Framework
Expeditious Saliency-guided Mix-up through Random Gradient Thresholding
Minh-Long Luu, Zeyi Huang, Eric P.Xing, Yong Jae Lee, Haohan Wang
2nd Practical-DL Workshop @ AAAI’23 [Paper] [Code]R-Mix and R-LMix Framework
SMMix: Self-Motivated Image Mixing for Vision Transformers
Mengzhao Chen, Mingbao Lin, ZhiHang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji
ICCV’2023 [Paper] [Code]SMMix Framework
Teach me how to Interpolate a Myriad of Embeddings
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Arxiv’2022 [Paper]MultiMix Framework
GradSalMix: Gradient Saliency-Based Mix for Image Data Augmentation
Tao Hong, Ya Wang, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Jinwen Ma
ICME’2023 [Paper]GradSalMix Framework
Label Mixup Methods¶
mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
ICLR’2018 [Paper] [Code]CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, Youngjoon Yoo
ICCV’2019 [Paper] [Code]Metamixup: Learning adaptive interpolation policy of mixup with metalearning
Zhijun Mai, Guosheng Hu, Dexiong Chen, Fumin Shen, Heng Tao Shen
TNNLS’2021 [Paper]MetaMixup Framework
Mixup Without Hesitation
Hao Yu, Huanyu Wang, Jianxin Wu
ICIG’2022 [Paper] [Code]Combining Ensembles and Data Augmentation can Harm your Calibration
Yeming Wen, Ghassen Jerfel, Rafael Muller, Michael W. Dusenberry, Jasper Snoek, Balaji Lakshminarayanan, Dustin Tran
ICLR’2021 [Paper] [Code]CAMixup Framework
Combining Ensembles and Data Augmentation can Harm your Calibration
Zihang Jiang, Qibin Hou, Li Yuan, Daquan Zhou, Yujun Shi, Xiaojie Jin, Anran Wang, Jiashi Feng
NIPS’2021 [Paper] [Code]TokenLabeling Framework
Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing
Joonhyung Park, June Yong Yang, Jinwoo Shin, Sung Ju Hwang, Eunho Yang
AAAI’2022 [Paper]Saliency Grafting Framework
TransMix: Attend to Mix for Vision Transformers
Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai
CVPR’2022 [Paper] [Code]TransMix Framework
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee
ArXiv’2022 [Paper]GenLabel Framework
Decoupled Mixup for Data-efficient Learning
Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
NIPS’2023 [Paper] [Code]DecoupleMix Framework
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu
ECCV’2022 [Paper] [Code]TokenMix Framework
Optimizing Random Mixup with Gaussian Differential Privacy
Donghao Li, Yang Cao, Yuan Yao
arXiv’2022 [Paper]TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers
Hyeong Kyu Choi, Joonmyung Choi, Hyunwoo J. Kim
NIPS’2022 [Paper] [Code]TokenMixup Framework
Token-Label Alignment for Vision Transformers
Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
arXiv’2022 [Paper] [Code]TL-Align Framework
LUMix: Improving Mixup by Better Modelling Label Uncertainty
Shuyang Sun, Jie-Neng Chen, Ruifei He, Alan Yuille, Philip Torr, Song Bai
arXiv’2022 [Paper] [Code]LUMix Framework
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Vikas Verma, Sarthak Mittal, Wai Hoh Tang, Hieu Pham, Juho Kannala, Yoshua Bengio, Arno Solin, Kenji Kawaguchi
arXiv’2022 [Paper]MixupE Framework
Infinite Class Mixup
Thomas Mensink, Pascal Mettes
arXiv’2023 [Paper]IC-Mixup Framework
Semantic Equivariant Mixup
Zongbo Han, Tianchi Xie, Bingzhe Wu, Qinghua Hu, Changqing Zhang
arXiv’2023 [Paper]SEM Framework
RankMixup: Ranking-Based Mixup Training for Network Calibration
Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham
ICCV’2023 [Paper] [Code]RankMixup Framework
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Xingyu Li, Bo Tang
arXiv’2023 [Paper]G-Mix Framework
Analysis of Mixup¶
Sunil Thulasidasan, Gopinath Chennupati, Jeff Bilmes, Tanmoy Bhattacharya, Sarah Michalak.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks. [NIPS’2019] [code]
Framework
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert.
On Mixup Regularization. [ArXiv’2020]
Framework
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou.
How Does Mixup Help With Robustness and Generalization? [ICLR’2021]
Framework
Muthu Chidambaram, Xiang Wang, Yuzheng Hu, Chenwei Wu, Rong Ge.
Framework
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, James Zou.
When and How Mixup Improves Calibration. [ICML’2022]
Framework
Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao.
Over-Training with Mixup May Hurt Generalization. [ICLR’2023]
Framework
Junsoo Oh, Chulhee Yun.
Provable Benefit of Mixup for Finding Optimal Decision Boundaries. [ICML’2023]
Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang.
On the Pitfall of Mixup for Uncertainty Calibration. [CVPR’2023]
Hongjun Choi, Eun Som Jeon, Ankita Shukla, Pavan Turaga.
Soyoun Won, Sung-Ho Bae, Seong Tae Kim.
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability. [arXiv’2023]
Survey¶
A survey on Image Data Augmentation for Deep Learning
Connor Shorten and Taghi Khoshgoftaar
Journal of Big Data’2019 [Paper]Survey: Image Mixing and Deleting for Data Augmentation
Humza Naveed, Saeed Anwar, Munawar Hayat, Kashif Javed, Ajmal Mian
ArXiv’2021 [Paper] [Code]An overview of mixing augmentation methods and augmentation strategies
Dominik Lewy and Jacek Ma ́ndziuk
Artificial Intelligence Review’2022 [Paper]Image Data Augmentation for Deep Learning: A Survey
Suorong Yang, Weikang Xiao, Mengcheng Zhang, Suhan Guo, Jian Zhao, Furao Shen
ArXiv’2022 [Paper]A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainability
Chengtai Cao, Fan Zhou, Yurou Dai, Jianping Wang
ArXiv’2022 [Paper] [Code]
Contribution¶
Feel free to send pull requests to add more links with the following Markdown format. Notice that the Abbreviation, the code link, and the figure link are optional attributes. Current contributors include: Siyuan Li (@Lupin1998) and Zicheng Liu (@pone7).
* **TITLE**<br>
*AUTHER*<br>
PUBLISH'YEAR [[Paper](link)] [[Code](link)]
<details close>
<summary>ABBREVIATION Framework</summary>
<p align="center"><img width="90%" src="link_to_image" /></p>
</details>