# Learning Imbalanced Datasets with Maximum Margin Loss

arXiv: https://arxiv.org/abs/2206.05380 · HTML: https://mlsanity.com/paper/2206.05380/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 24 references that are in the map.
- First posted on arXiv 11 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Scene Graph Generation by Iterative Message Passing](https://mlsanity.com/paper/1701.02426/) (2017)
- [BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition](https://mlsanity.com/paper/1912.02413/) (2019)
- [Large-Margin Softmax Loss for Convolutional Neural Networks](https://mlsanity.com/paper/1612.02295/) (2016)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [The Devil is in the Tails: Fine-grained Classification in the Wild](https://mlsanity.com/paper/1709.01450/) (2017)
- [Striking the Right Balance with Uncertainty](https://mlsanity.com/paper/1901.07590/) (2019)

## Built on by (most cited first, 9 of 9)

- [Class-Balancing Diffusion Models](https://mlsanity.com/paper/2305.00562/) (2023)
- [Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation](https://mlsanity.com/paper/2301.00351/) (2023)
- [Enlarged Large Margin Loss for Imbalanced Classification](https://mlsanity.com/paper/2306.09132/) (2023)
- [Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions](https://mlsanity.com/paper/2402.05400/) (2024)
- [CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram Classification](https://mlsanity.com/paper/2403.09108/) (2024)
- [Training Over a Distribution of Hyperparameters for Enhanced Performance and Adaptability on Imbalanced Classification](https://mlsanity.com/paper/2410.03588/) (2024)
- [Long-Tailed Data Classification by Increasing and Decreasing Neurons During Training](https://mlsanity.com/paper/2507.09940/) (2025)
- [GAS: Enhancing Reward-Cost Balance of Generative Model-assisted Offline Safe RL](https://mlsanity.com/paper/2602.05323/) (2026)
- [ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin](https://mlsanity.com/paper/2605.13517/) (2026)

## Abstract (arXiv)

A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization. For a good generalization of the minority classes, we design a new Maximum Margin (MM) loss function, motivated by minimizing a margin-based generalization bound through the shifting decision bound. The theoretically-principled label-distribution-aware margin (LDAM) loss was successfully applied with prior strategies such as re-weighting or re-sampling along with the effective training schedule. However, they did not investigate the maximum margin loss function yet. In this study, we investigate the performances of two types of hard maximum margin-based decision boundary shift with LDAM's training schedule on artificially imbalanced CIFAR-10/100 for fair comparisons and effectiveness.