mlsanityData to 14 Oct 2026

Class Rectification Hard Mining for Imbalanced Deep Learning

arXiv 1712.03162 ↗First posted 8 Dec 2017cs.CVcited by 67

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Abstract (arXiv)

Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes. To address this problem, we formulate a novel scheme for batch incremental hard sample mining of minority attribute classes from imbalanced large scale training data. We develop an end-to-end deep learning framework capable of avoiding the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes. This is made possible by introducing a Class Rectification Loss (CRL) regularising algorithm. We demonstrate the advantages and scalability of CRL over existing state-of-the-art attribute recognition and imbalanced data learning models on two large scale imbalanced benchmark datasets, the CelebA facial attribute dataset and the X-Domain clothing attribute dataset.

Key facts. Cited by 67 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2017.

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Citing papers per year: 2021: 19; 2022: 16; 2023: 14; 2024: 6; 2025: 6; 2026: 6.

67 papers.

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  1. 2021Deep Long-Tailed Learning: A Surveycited by 417
  2. 2021Influence-Balanced Loss for Imbalanced Visual Classificationcited by 91
  3. 2022A Survey on Long-Tailed Visual Recognitioncited by 75
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  6. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 60
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  9. 2021Keyframe-Focused Visual Imitation Learningcited by 25
  10. 2022ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classificationcited by 25
  11. 2021Procrustean Training for Imbalanced Deep Learningcited by 24
  12. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
38 more
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  5. 2021Delving into Sample Loss Curve to Embrace Noisy and Imbalanced Datacited by 16
  6. 2021Generalized Data Weighting via Class-level Gradient Manipulationcited by 15
  7. 2023LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognitioncited by 15
  8. 2023Constraint and Union for Partially-Supervised Temporal Sentence Groundingcited by 14
  9. 2022FORML: Learning to Reweight Data for Fairnesscited by 12
  10. 2025A Comprehensive Survey on Imbalanced Data Learningcited by 10
  11. 2023Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifoldscited by 9
  12. 2023SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tailcited by 9
  13. 2024Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspectivecited by 9
  14. 2023Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Imagescited by 7
  15. 2023Threshold-Consistent Margin Loss for Open-World Deep Metric Learningcited by 7
  16. 2023Zone Evaluation: Revealing Spatial Bias in Object Detectioncited by 7
  17. 2021Improving Tail-Class Representation with Centroid Contrastive Learningcited by 6
  18. 2023Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimizationcited by 6
  19. 2023Invariant Feature Regularization for Fair Face Recognitioncited by 6
  20. 2021DIVA: Dataset Derivative of a Learning Taskcited by 5
  21. 2021Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learningcited by 5
  22. 2024AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasetscited by 5
  23. 2023Rethinking Class Imbalance in Machine Learningcited by 4
  24. 2021Large Scale Long-tailed Product Recognition System at Alibabacited by 3
  25. 2022Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Imagescited by 3
  26. 2022MetricBERT: Text Representation Learning via Self-Supervised Triplet Trainingcited by 3
  27. 2022Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognitioncited by 3
  28. 2023RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithmcited by 3
  29. 2023Learning to Select Pivotal Samples for Meta Re-weightingcited by 3
  30. 2024The Power of Few: Accelerating and Enhancing Data Reweighting with Coreset Selectioncited by 3
  31. 2025From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learningcited by 3
  32. 2021Discriminative-Generative Dual Memory Video Anomaly Detectioncited by 2
  33. 2021Energy Aligning for Biased Modelscited by 2
  34. 2022Learning Muti-expert Distribution Calibration for Long-tailed Video Classificationcited by 2
  35. 2022Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Datacited by 2
  36. 2022MIME: Minority Inclusion for Majority Group Enhancement of AI Performancecited by 2
  37. 2022Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognitioncited by 2
  38. 2024Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learningcited by 2

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