# Class-Imbalanced Complementary-Label Learning via Weighted Loss

arXiv: https://arxiv.org/abs/2209.14189 · HTML: https://mlsanity.com/paper/2209.14189/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 24 of 24)

- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (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)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Dimensionality-Driven Learning with Noisy Labels](https://mlsanity.com/paper/1806.02612/) (2018)
- [NLNL: Negative Learning for Noisy Labels](https://mlsanity.com/paper/1908.07387/) (2019)
- [Progressive Identification of True Labels for Partial-Label Learning](https://mlsanity.com/paper/2002.08053/) (2020)
- [Learning with Biased Complementary Labels](https://mlsanity.com/paper/1711.09535/) (2017)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [Semi-Supervised Learning with Normalizing Flows](https://mlsanity.com/paper/1912.13025/) (2019)
- [Imbalanced Deep Learning by Minority Class Incremental Rectification](https://mlsanity.com/paper/1804.10851/) (2018)
- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [On Symmetric Losses for Learning from Corrupted Labels](https://mlsanity.com/paper/1901.09314/) (2019)
- [Learning with Multiple Complementary Labels](https://mlsanity.com/paper/1912.12927/) (2019)
- [Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data](https://mlsanity.com/paper/2101.08030/) (2021)
- [Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels](https://mlsanity.com/paper/2007.02235/) (2020)
- [Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning](https://mlsanity.com/paper/1705.01708/) (2017)
- [Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images](https://mlsanity.com/paper/2112.12004/) (2021)
- [Generative-Discriminative Complementary Learning](https://mlsanity.com/paper/1904.01612/) (2019)
- [The information-theoretic value of unlabeled data in semi-supervised learning](https://mlsanity.com/paper/1901.05515/) (2019)
- [Clustering Unclustered Data: Unsupervised Binary Labeling of Two Datasets Having Different Class Balances](https://mlsanity.com/paper/1305.0103/) (2013)
- [Online Multiclass Classification Based on Prediction Margin for Partial Feedback](https://mlsanity.com/paper/1902.01056/) (2019)

## Built on by (most cited first, 7 of 7)

- [Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical](https://mlsanity.com/paper/2311.15502/) (2023)
- [Learning from True-False Labels via Multi-modal Prompt Retrieving](https://mlsanity.com/paper/2405.15228/) (2024)
- [Learning from Stochastic Labels](https://mlsanity.com/paper/2302.00299/) (2023)
- [Determined Multi-Label Learning via Similarity-Based Prompt](https://mlsanity.com/paper/2403.16482/) (2024)
- [Learning from Complementary Features](https://mlsanity.com/paper/2408.14788/) (2024)
- [Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes](https://mlsanity.com/paper/2605.15586/) (2026)
- [Recent advances in weakly supervised learning: New supervision paradigms, assumption relaxations, and practical solutions](https://mlsanity.com/paper/2608.06896/) (2026)

## Abstract (arXiv)

Complementary-label learning (CLL) is widely used in weakly supervised classification, but it faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples. In such scenarios, the number of samples in one class is considerably lower than in other classes, which consequently leads to a decline in the accuracy of predictions. Unfortunately, existing CLL approaches have not investigate this problem. To alleviate this challenge, we propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification. To tackle this problem, we propose a novel CLL approach called Weighted Complementary-Label Learning (WCLL). The proposed method models a weighted empirical risk minimization loss by utilizing the class-imbalanced complementary labels, which is also applicable to multi-class imbalanced training samples. Furthermore, we derive an estimation error bound to provide theoretical assurance. To evaluate our approach, we conduct extensive experiments on several widely-used benchmark datasets and a real-world dataset, and compare our method with existing state-of-the-art methods. The proposed approach shows significant improvement in these datasets, even in the case of multiple class-imbalanced scenarios. Notably, the proposed method not only utilizes complementary labels to train a classifier but also solves the problem of class imbalance.