# Toward Robustness in Multi-label Classification: A Data Augmentation Strategy against Imbalance and Noise

arXiv: https://arxiv.org/abs/2312.07087 · HTML: https://mlsanity.com/paper/2312.07087/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [DivideMix: Learning with Noisy Labels as Semi-supervised Learning](https://mlsanity.com/paper/2002.07394/) (2020)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [Query2Label: A Simple Transformer Way to Multi-Label Classification](https://mlsanity.com/paper/2107.10834/) (2021)
- [General Multi-label Image Classification with Transformers](https://mlsanity.com/paper/2011.14027/) (2020)
- [Learning a Deep ConvNet for Multi-label Classification with Partial Labels](https://mlsanity.com/paper/1902.09720/) (2019)
- [Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets](https://mlsanity.com/paper/2007.09654/) (2020)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Understanding and Improving Early Stopping for Learning with Noisy Labels](https://mlsanity.com/paper/2106.15853/) (2021)
- [The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification](https://mlsanity.com/paper/2112.00412/) (2021)
- [Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise](https://mlsanity.com/paper/2012.05458/) (2020)
- [Multi-Label Learning from Single Positive Labels](https://mlsanity.com/paper/2106.09708/) (2021)
- [ML-Decoder: Scalable and Versatile Classification Head](https://mlsanity.com/paper/2111.12933/) (2021)
- [Balanced-MixUp for Highly Imbalanced Medical Image Classification](https://mlsanity.com/paper/2109.09850/) (2021)
- [Robust Long-Tailed Learning under Label Noise](https://mlsanity.com/paper/2108.11569/) (2021)
- [Fine-Grained Classification with Noisy Labels](https://mlsanity.com/paper/2303.02404/) (2023)
- [Evaluating Multi-label Classifiers with Noisy Labels](https://mlsanity.com/paper/2102.08427/) (2021)
- [SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail](https://mlsanity.com/paper/2304.00101/) (2023)
- [Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation](https://mlsanity.com/paper/2203.10858/) (2022)

## Built on by (most cited first, 11 of 11)

- [Curiosity Meets Cooperation: A Game-Theoretic Approach to Long-Tail Multi-Label Learning](https://mlsanity.com/paper/2510.17520/) (2025)
- [Ensemble Methods for Sequence Classification with Hidden Markov Models](https://mlsanity.com/paper/2409.07619/) (2024)
- [A Bilevel Optimization Framework for Imbalanced Data Classification](https://mlsanity.com/paper/2410.11171/) (2024)
- [Model-Agnostic Meta Learning for Class Imbalance Adaptation](https://mlsanity.com/paper/2604.18759/) (2026)
- [Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification](https://mlsanity.com/paper/2405.15860/) (2024)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [Sampling Imbalanced Data with Multi-objective Bilevel Optimization](https://mlsanity.com/paper/2506.11315/) (2025)
- [On the Performance of Concept Probing: The Influence of the Data (Extended Version)](https://mlsanity.com/paper/2507.18550/) (2025)
- [Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification](https://mlsanity.com/paper/2601.08446/) (2026)
- [A Scalable Curiosity-Driven Game-Theoretic Framework for Long-Tail Multi-Label Learning in Data Mining](https://mlsanity.com/paper/2602.15330/) (2026)
- [Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling](https://mlsanity.com/paper/2606.05927/) (2026)

## Abstract (arXiv)

Multi-label classification poses challenges due to imbalanced and noisy labels in training data. We propose a unified data augmentation method, named BalanceMix, to address these challenges. Our approach includes two samplers for imbalanced labels, generating minority-augmented instances with high diversity. It also refines multi-labels at the label-wise granularity, categorizing noisy labels as clean, re-labeled, or ambiguous for robust optimization. Extensive experiments on three benchmark datasets demonstrate that BalanceMix outperforms existing state-of-the-art methods. We release the code at https://github.com/DISL-Lab/BalanceMix.