# Enhancing Label Sharing Efficiency in Complementary-Label Learning with Label Augmentation

arXiv: https://arxiv.org/abs/2305.08344 · HTML: https://mlsanity.com/paper/2305.08344/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Learning with Biased Complementary Labels](https://mlsanity.com/paper/1711.09535/) (2017)
- [Learning from Complementary Labels](https://mlsanity.com/paper/1705.07541/) (2017)
- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels](https://mlsanity.com/paper/2007.02235/) (2020)
- [Reduction from Complementary-Label Learning to Probability Estimates](https://mlsanity.com/paper/2209.09500/) (2022)
- [Boosting Semi-Supervised Learning with Contrastive Complementary Labeling](https://mlsanity.com/paper/2212.06643/) (2022)

## Abstract (arXiv)

Complementary-label Learning (CLL) is a form of weakly supervised learning that trains an ordinary classifier using only complementary labels, which are the classes that certain instances do not belong to. While existing CLL studies typically use novel loss functions or training techniques to solve this problem, few studies focus on how complementary labels collectively provide information to train the ordinary classifier. In this paper, we fill the gap by analyzing the implicit sharing of complementary labels on nearby instances during training. Our analysis reveals that the efficiency of implicit label sharing is closely related to the performance of existing CLL models. Based on this analysis, we propose a novel technique that enhances the sharing efficiency via complementary-label augmentation, which explicitly propagates additional complementary labels to each instance. We carefully design the augmentation process to enrich the data with new and accurate complementary labels, which provide CLL models with fresh and valuable information to enhance the sharing efficiency. We then verify our proposed technique by conducting thorough experiments on both synthetic and real-world datasets. Our results confirm that complementary-label augmentation can systematically improve empirical performance over state-of-the-art CLL models.