# MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

arXiv: https://arxiv.org/abs/2106.09643 · HTML: https://mlsanity.com/paper/2106.09643/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [How Does Gender Balance In Training Data Affect Face Recognition Accuracy?](https://mlsanity.com/paper/2002.02934/) (2020)
- [Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation](https://mlsanity.com/paper/1907.10982/) (2019)
- [Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data](https://mlsanity.com/paper/1908.09092/) (2019)
- [Fair Meta-Learning For Few-Shot Classification](https://mlsanity.com/paper/2009.13516/) (2020)
- [Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation](https://mlsanity.com/paper/1901.08394/) (2019)
- [Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks](https://mlsanity.com/paper/2004.02182/) (2020)
- [Recurrent Convolutional Neural Networks help to predict location of Earthquakes](https://mlsanity.com/paper/2004.09140/) (2020)
- [Removing Class Imbalance using Polarity-GAN: An Uncertainty Sampling Approach](https://mlsanity.com/paper/2012.04937/) (2020)

## Built on by (most cited first, 1 of 1)

- [Simplifying Neural Network Training Under Class Imbalance](https://mlsanity.com/paper/2312.02517/) (2023)

## Abstract (arXiv)

Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications. Standard techniques for handling class-imbalance usually work by training on a re-weighted loss or on re-balanced data. Unfortunately, training overparameterized neural networks on such objectives causes rapid memorization of minority class data. To avoid this trap, we harness meta-learning, which uses both an ''outer-loop'' and an ''inner-loop'' loss, each of which may be balanced using different strategies. We evaluate our method, MetaBalance, on image classification, credit-card fraud detection, loan default prediction, and facial recognition tasks with severely imbalanced data, and we find that MetaBalance outperforms a wide array of popular re-sampling strategies.