mlsanityData to 7 Oct 2026

M2m: Imbalanced Classification via Major-to-minor Translation

arXiv 2004.00431 ↗First posted 1 Apr 2020cs.CV cs.LG stat.MLcited by 134

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Abstract (arXiv)

In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples (e.g., images) from more-frequent classes. This simple approach enables a classifier to learn more generalizable features of minority classes, by transferring and leveraging the diversity of the majority information. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods. The performance of our method even surpasses those of previous state-of-the-art methods for the imbalanced classification.

Key facts. Cited by 134 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2020.

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Citing papers per year: 2021: 29; 2022: 23; 2023: 36; 2024: 25; 2025: 14; 2026: 7.

134 papers.

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  1. 2021Deep Long-Tailed Learning: A Surveycited by 416
  2. 2021DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentationcited by 299
  3. 2021CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learningcited by 174
  4. 2021Targeted Supervised Contrastive Learning for Long-Tailed Recognitioncited by 132
  5. 2021Contrastive Learning based Hybrid Networks for Long-Tailed Image Classificationcited by 130
  6. 2021The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classificationcited by 100
  7. 2021DENSE: Data-Free One-Shot Federated Learningcited by 100
  8. 2022Balanced MSE for Imbalanced Visual Regressioncited by 89
  9. 2021ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shotcited by 86
  10. 2021Distributional Robustness Loss for Long-tail Learningcited by 84
  11. 2021FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentationcited by 76
  12. 2022A Survey on Long-Tailed Visual Recognitioncited by 75
38 more
  1. 2021Self Supervision to Distillation for Long-Tailed Visual Recognitioncited by 73
  2. 2021ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learningcited by 65
  3. 2021VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognitioncited by 65
  4. 2022Nested Collaborative Learning for Long-Tailed Visual Recognitioncited by 63
  5. 2022Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Featurescited by 63
  6. 2021DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learningcited by 51
  7. 2021Trustworthy Long-Tailed Classificationcited by 51
  8. 2021Adversarial Robustness under Long-Tailed Distributioncited by 50
  9. 2021On Exposing the Challenging Long Tail in Future Prediction of Traffic Actorscited by 48
  10. 2023Class-Balancing Diffusion Modelscited by 47
  11. 2021Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspectivecited by 39
  12. 2023How Re-sampling Helps for Long-Tail Learning?cited by 36
  13. 2022Invariant Feature Learning for Generalized Long-Tailed Classificationcited by 34
  14. 2022Learning Imbalanced Data with Vision Transformerscited by 34
  15. 2023Fill-Up: Balancing Long-Tailed Data with Generative Modelscited by 34
  16. 2022Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentationcited by 33
  17. 2022SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learningcited by 30
  18. 2023CUDA: Curriculum of Data Augmentation for Long-Tailed Recognitioncited by 30
  19. 2021Class-Balanced Distillation for Long-Tailed Visual Recognitioncited by 27
  20. 2024A Systematic Review on Long-Tailed Learningcited by 27
  21. 2023Balancing Logit Variation for Long-tailed Semantic Segmentationcited by 26
  22. 2023Subclass-balancing Contrastive Learning for Long-tailed Recognitioncited by 26
  23. 2022Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasetscited by 25
  24. 2022Constructing Balance from Imbalance for Long-tailed Image Recognitioncited by 25
  25. 2021Procrustean Training for Imbalanced Deep Learningcited by 24
  26. 2023Long-Tailed Partial Label Learning via Dynamic Rebalancingcited by 22
  27. 2023Use Your Head: Improving Long-Tail Video Recognitioncited by 22
  28. 2023Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavationcited by 22
  29. 2023Feature Fusion from Head to Tail for Long-Tailed Visual Recognitioncited by 20
  30. 2022Improving the Intra-class Long-tail in 3D Detection via Rare Example Miningcited by 19
  31. 2023GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classificationcited by 19
  32. 2022Imbalanced Semi-supervised Learning with Bias Adaptive Classifiercited by 18
  33. 2023Simplifying Neural Network Training Under Class Imbalancecited by 18
  34. 2021Graph Classification by Mixture of Diverse Expertscited by 16
  35. 2021Rethinking Re-Sampling in Imbalanced Semi-Supervised Learningcited by 16
  36. 2021Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Imagescited by 16
  37. 2023No One Left Behind: Improving the Worst Categories in Long-Tailed Learningcited by 14
  38. 2023NCL++: Nested Collaborative Learning for Long-Tailed Visual Recognitioncited by 14

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