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.
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- Awesome-Mixup
- Introduction
- Table of Contents
- Fundermental 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
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mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
ICLR'2018 [Paper] [Code] -
Between-class Learning for Image Classification
Yuji Tokozume, Yoshitaka Ushiku, Tatsuya Harada
CVPR'2018 [Paper] [Code] -
MixUp as Locally Linear Out-Of-Manifold Regularization
Hongyu Guo, Yongyi Mao, Richong Zhang
AAAI'2019 [Paper] -
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] -
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] -
Improved Mixed-Example Data Augmentation
Cecilia Summers, Michael J. Dinneen
WACV'2019 [Paper] [Code] -
FMix: Enhancing Mixed Sample Data Augmentation
Ethan Harris, Antonia Marcu, Matthew Painter, Mahesan Niranjan, Adam Prügel-Bennett, Jonathon Hare
Arixv'2020 [Paper] [Code] -
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] -
PatchUp: A Regularization Technique for Convolutional Neural Networks
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar
Arxiv'2020 [Paper] [Code] -
GridMix: Strong regularization through local context mapping
Kyungjune Baek, Duhyeon Bang, Hyunjung Shim
Pattern Recognition'2021 [Paper] [Code] -
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] -
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] -
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] -
DJMix: Unsupervised Task-agnostic Augmentation for Improving Robustness
Ryuichiro Hataya, Hideki Nakayama
Arxiv'2021 [Paper] -
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] -
StyleMix: Separating Content and Style for Enhanced Data Augmentation
Minui Hong, Jinwoo Choi, Gunhee Kim
CVPR'2021 [Paper] [Code] -
Domain Generalization with MixStyle
Kaiyang Zhou, Yongxin Yang, Yu Qiao, Tao Xiang
ICLR'2021 [Paper] [Code] -
On Feature Normalization and Data Augmentation
Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger
CVPR'2021 [Paper] [Code] -
Guided Interpolation for Adversarial Training
Chen Chen, Jingfeng Zhang, Xilie Xu, Tianlei Hu, Gang Niu, Gang Chen, Masashi Sugiyama
ArXiv'2021 [Paper] -
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] -
Noisy Feature Mixup
Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney
ICLR'2022 [Paper] [Code] -
Preventing Manifold Intrusion with Locality: Local Mixup
Raphael Baena, Lucas Drumetz, Vincent Gripon
EUSIPCO'2022 [Paper] [Code] -
RandomMix: A mixed sample data augmentation method with multiple mixed modes
Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie
ArXiv'2022 [Paper] -
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation
Karim Hammoudi, Adnane Cabani, Bouthaina Slika, Halim Benhabiles, Fadi Dornaika, Mahmoud Melkemi
ArXiv'2022 [Paper] [Code] -
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] -
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
NIPS'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] -
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]
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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] -
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] -
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Shaoli Huang, Xinchao Wang, Dacheng Tao
AAAI'2021 [Paper] [Code] -
Attribute Mix: Semantic Data Augmentation for Fine Grained Recognition
Hao Li, Xiaopeng Zhang, Hongkai Xiong, Qi Tian
VCIP'2020 [Paper] -
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] -
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
ArXiv'2019 [Paper] -
AutoMix: Mixup Networks for Sample Interpolation via Cooperative Barycenter Learning
Jianchao Zhu, Liangliang Shi, Junchi Yan, Hongyuan Zha
ECCV'2020 [Paper] -
PuzzleMix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
ICML'2020 [Paper] [Code] -
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
Jang-Hyun Kim, Wonho Choo, Hosan Jeong, Hyun Oh Song
ICLR'2021 [Paper] [Code] -
SuperMix: Supervising the Mixing Data Augmentation
Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi
CVPR'2021 [Paper] [Code] -
Evolving Image Compositions for Feature Representation Learning
Paola Cascante-Bonilla, Arshdeep Sekhon, Yanjun Qi, Vicente Ordonez
BMVC'2021 [Paper] -
StackMix: A complementary Mix algorithm
John Chen, Samarth Sinha, Anastasios Kyrillidis
UAI'2022 [Paper] -
SalfMix: A Novel Single Image-Based Data Augmentation Technique Using a Saliency Map
Jaehyeop Choi, Chaehyeon Lee, Donggyu Lee, Heechul Jung
Sensor'2021 [Paper] -
k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien
ArXiv'2021 [Paper] -
AlignMix: Improving representation by interpolating aligned features
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
CVPR'2022 [Paper] [Code] -
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] -
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li
Arxiv'2021 [Paper] [Code] -
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] -
RecursiveMix: Mixed Learning with History
Lingfeng Yang, Xiang Li, Borui Zhao, Renjie Song, Jian Yang
NIPS'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] -
TransformMix: Learning Transformation and Mixing Strategies for Sample-mixing Data Augmentation
Tsz-Him Cheung, Dit-Yan Yeung.<\br> OpenReview'2023 [Paper] -
GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps
Minsoo Kang, Suhyun Kim
AAAI'2023 [Paper] -
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] -
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] -
SMMix: Self-Motivated Image Mixing for Vision Transformers
Mengzhao Chen, Mingbao Lin, ZhiHang Lin, Yuxin Zhang, Fei Chao, Rongrong Ji
ICCV'2023 [Paper] [Code]
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Embedding Space Interpolation Beyond Mini-Batch, Beyond Pairs and Beyond Examples
Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
NeurIPS'2023 [Paper] -
GradSalMix: Gradient Saliency-Based Mix for Image Data Augmentation
Tao Hong, Ya Wang, Xingwu Sun, Fengzong Lian, Zhanhui Kang, Jinwen Ma
ICME'2023 [Paper] -
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] -
Catch-Up Mix: Catch-Up Class for Struggling Filters in CNN
Minsoo Kang, Minkoo Kang, Suhyun Kim
AAAI'2024 [Paper] -
Adversarial AutoMixup
Huafeng Qin, Xin Jin, Yun Jiang, Mounim A. El-Yacoubi, Xinbo Gao
ICLR'2024 [Paper] [Code]
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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] -
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] -
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] -
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] -
TransMix: Attend to Mix for Vision Transformers
Jie-Neng Chen, Shuyang Sun, Ju He, Philip Torr, Alan Yuille, Song Bai
CVPR'2022 [Paper] [Code] -
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen, Jaekyun Moon, Dimitris Papailiopoulos, Kangwook Lee
ArXiv'2022 [Paper] -
Harnessing Hard Mixed Samples with Decoupled Regularizer
Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
NIPS'2023 [Paper] [Code] -
TokenMix: Rethinking Image Mixing for Data Augmentation in Vision Transformers
Jihao Liu, Boxiao Liu, Hang Zhou, Hongsheng Li, Yu Liu
ECCV'2022 [Paper] [Code] -
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] -
Token-Label Alignment for Vision Transformers
Han Xiao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
arXiv'2022 [Paper] [Code] -
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] -
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] -
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] -
RankMixup: Ranking-Based Mixup Training for Network Calibration
Jongyoun Noh, Hyekang Park, Junghyup Lee, Bumsub Ham
ICCV'2023 [Paper] [Code] -
G-Mix: A Generalized Mixup Learning Framework Towards Flat Minima
Xingyu Li, Bo Tang
arXiv'2023 [Paper]
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MixCo: Mix-up Contrastive Learning for Visual Representation
Sungnyun Kim, Gihun Lee, Sangmin Bae, Se-Young Yun
NIPSW'2020 [Paper] [Code] -
Hard Negative Mixing for Contrastive Learning
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus
NIPS'2020 [Paper] [Code] -
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] -
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] -
Beyond Single Instance Multi-view Unsupervised Representation Learning
Xiangxiang Chu, Xiaohang Zhan, Xiaolin Wei
BMVC'2022 [Paper] -
Improving Contrastive Learning by Visualizing Feature Transformation
Rui Zhu, Bingchen Zhao, Jingen Liu, Zhenglong Sun, Chang Wen Chen
ICCV'2021 [Paper] [Code] -
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] -
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] -
Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup
Siyuan Li, Zicheng Liu, Di Wu, Zihan Liu, Stan Z. Li
Arxiv'2021 [Paper] [Code] -
MixSiam: A Mixture-based Approach to Self-supervised Representation Learning
Xiaoyang Guo, Tianhao Zhao, Yutian Lin, Bo Du
OpenReview'2021 [Paper] -
Mix-up Self-Supervised Learning for Contrast-agnostic Applications
Yichen Zhang, Yifang Yin, Ying Zhang, Roger Zimmermann
ICME'2021 [Paper] -
Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi, Hieu Pham, Quoc V. Le
ICML'2021 [Paper] -
Center-wise Local Image Mixture For Contrastive Representation Learning
Hao Li, Xiaopeng Zhang, Hongkai Xiong
BMVC'2021 [Paper] -
Contrastive-mixup Learning for Improved Speaker Verification
Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke
ICASSP'2022 [Paper] -
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive Learning
Jun Xia, Lirong Wu, Ge Wang, Jintao Chen, Stan Z.Li
ICML'2022 [Paper] [Code] -
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] -
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] -
On the Importance of Asymmetry for Siamese Representation Learning
Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen
CVPR'2022 [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] -
CropMix: Sampling a Rich Input Distribution via Multi-Scale Cropping
Junlin Han, Lars Petersson, Hongdong Li, Ian Reid
ArXiv'2022 [Paper] [Code] -
i-MAE: Are Latent Representations in Masked Autoencoders Linearly Separable
Kevin Zhang, Zhiqiang Shen
ArXiv'2022 [Paper] [Code] -
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] -
Mixed Autoencoder for Self-supervised Visual Representation Learning
Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung
CVPR'2023 [Paper] -
Inter-Instance Similarity Modeling for Contrastive Learning
Chengchao Shen, Dawei Liu, Hao Tang, Zhe Qu, Jianxin Wang
ArXiv'2023 [Paper] [Code] -
Guarding Barlow Twins Against Overfitting with Mixed Samples
Wele Gedara Chaminda Bandara, Celso M. De Melo, Vishal M. Patel
ArXiv'2023 [Paper] [Code]
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MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel
NIPS'2019 [Paper] [Code] -
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Ke Sun, Bing Yu, Zhouchen Lin, Zhanxing Zhu
ArXiv'2019 [Paper] -
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] -
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li, Richard Socher, Steven C.H. Hoi
ICLR'2020 [Paper] [Code] -
Epsilon Consistent Mixup: Structural Regularization with an Adaptive Consistency-Interpolation Tradeoff
Vincent Pisztora, Yanglan Ou, Xiaolei Huang, Francesca Chiaromonte, Jia Li
ArXiv'2021 [Paper] -
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] -
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] -
Harnessing Hard Mixed Samples with Decoupled Regularizer
Zicheng Liu, Siyuan Li, Ge Wang, Cheng Tan, Lirong Wu, Stan Z. Li
NIPS'2023 [Paper] [Code] -
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] -
LaserMix for Semi-Supervised LiDAR Semantic Segmentation
Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu
CVPR'2023 [Paper] [Code] [project] -
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] -
Mixed Pseudo Labels for Semi-Supervised Object Detection
Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang
ArXiv'2023 [Paper] [Code]
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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] -
Tailoring Mixup to Data using Kernel Warping functions
Quentin Bouniot, Pavlo Mozharovskyi, Florence d'Alché-Buc
ArXiv'2023 [Paper] [Code] -
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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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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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]
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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] -
On Mixup Regularization
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton, Jean-Philippe Vert
ArXiv'2020 [Paper] -
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou
ICLR'2021 [Paper] -
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] -
Over-Training with Mixup May Hurt Generalization
Zixuan Liu, Ziqiao Wang, Hongyu Guo, Yongyi Mao
ICLR'2023 [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] -
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]
-
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]
-
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]
-
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]
- 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]
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
!
This project is released under the Apache 2.0 license.
This repository is built using the OpenMixup library and Awesome README repository.
- 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.