# libcll: an Extendable Python Toolkit for Complementary-Label Learning

arXiv: https://arxiv.org/abs/2411.12276 · HTML: https://mlsanity.com/paper/2411.12276/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Complementary-Label Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/1810.04327/) (2018)
- [Learning with Multiple Complementary Labels](https://mlsanity.com/paper/1912.12927/) (2019)
- [Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical](https://mlsanity.com/paper/2311.15502/) (2023)
- [CLImage: Human-Annotated Datasets for Complementary-Label Learning](https://mlsanity.com/paper/2305.08295/) (2023)
- [Multi-Complementary and Unlabeled Learning for Arbitrary Losses and Models](https://mlsanity.com/paper/2001.04243/) (2020)

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

- [Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning](https://mlsanity.com/paper/2509.17971/) (2025)

## Abstract (arXiv)

Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does not belong to -- are provided to the learning algorithm. Despite CLL's increasing popularity, previous studies highlight two main challenges: (1) inconsistent results arising from varied assumptions on complementary label generation, and (2) high barriers to entry due to the lack of a standardized evaluation platform across datasets and algorithms. To address these challenges, we introduce textttlibcll, an extensible Python toolkit for CLL research. textttlibcll provides a universal interface that supports a wide range of generation assumptions, both synthetic and real-world datasets, and key CLL algorithms. The toolkit is designed to mitigate inconsistencies and streamline the research process, with easy installation, comprehensive usage guides, and quickstart tutorials that facilitate efficient adoption and implementation of CLL techniques. Extensive ablation studies conducted with textttlibcll demonstrate its utility in generating valuable insights to advance future CLL research.