# Imbalanced Deep Learning by Minority Class Incremental Rectification

arXiv: https://arxiv.org/abs/1804.10851 · HTML: https://mlsanity.com/paper/1804.10851/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2018.

## Built on by (most cited first, 30 of 65)

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- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
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- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2106.05682/) (2021)
- [AutoBalance: Optimized Loss Functions for Imbalanced Data](https://mlsanity.com/paper/2201.01212/) (2022)
- [On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors](https://mlsanity.com/paper/2103.12474/) (2021)
- [Crop mapping from image time series: deep learning with multi-scale label hierarchies](https://mlsanity.com/paper/2102.08820/) (2021)
- [Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation](https://mlsanity.com/paper/2102.10365/) (2021)
- [Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization](https://mlsanity.com/paper/2306.09264/) (2023)
- [A Systematic Review on Long-Tailed Learning](https://mlsanity.com/paper/2408.00483/) (2024)
- [On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond](https://mlsanity.com/paper/2203.09513/) (2022)
- [Subclass-balancing Contrastive Learning for Long-tailed Recognition](https://mlsanity.com/paper/2306.15925/) (2023)
- [Survey of Social Bias in Vision-Language Models](https://mlsanity.com/paper/2309.14381/) (2023)
- [Procrustean Training for Imbalanced Deep Learning](https://mlsanity.com/paper/2104.01769/) (2021)
- [Class-Imbalanced Learning on Graphs: A Survey](https://mlsanity.com/paper/2304.04300/) (2023)
- [Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighting](https://mlsanity.com/paper/2306.13085/) (2023)
- [A Theoretical Analysis of the Learning Dynamics under Class Imbalance](https://mlsanity.com/paper/2207.00391/) (2022)
- [Graph Neural Network with Curriculum Learning for Imbalanced Node Classification](https://mlsanity.com/paper/2202.02529/) (2022)
- [Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images](https://mlsanity.com/paper/2112.08810/) (2021)
- [RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data](https://mlsanity.com/paper/2103.00221/) (2021)
- [Semantic Segmentation with Labeling Uncertainty and Class Imbalance](https://mlsanity.com/paper/2102.04566/) (2021)
- [Generative Oversampling for Imbalanced Data via Majority-Guided VAE](https://mlsanity.com/paper/2302.10910/) (2023)
- [SOS! Self-supervised Learning Over Sets Of Handled Objects In Egocentric Action Recognition](https://mlsanity.com/paper/2204.04796/) (2022)
- [Robust Asymmetric Loss for Multi-Label Long-Tailed Learning](https://mlsanity.com/paper/2308.05542/) (2023)
- [MiLe Loss: a New Entropy-Weighed Loss for Mitigating the Bias of Learning Difficulties in Large Language Models](https://mlsanity.com/paper/2310.19531/) (2023)
- [Improving Facial Attribute Recognition by Group and Graph Learning](https://mlsanity.com/paper/2105.13825/) (2021)
- [Perspectives on Incorporating Expert Feedback into Model Updates](https://mlsanity.com/paper/2205.06905/) (2022)

## Abstract (arXiv)

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.