Summary of Libcll: An Extendable Python Toolkit For Complementary-label Learning, by Nai-xuan Ye et al.
libcll: an Extendable Python Toolkit for Complementary-Label Learning
by Nai-Xuan Ye, Tan-Ha Mai, Hsiu-Hsuan Wang, Wei-I Lin, Hsuan-Tien Lin
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Complementary-label learning (CLL) is a type of weakly supervised machine learning for multiclass classification, where only complementary labels indicating classes an instance does not belong to are provided. The paradigm has gained popularity, but previous studies have highlighted challenges in obtaining consistent results and high barriers to entry due to the lack of a standardized evaluation platform. To address these challenges, we introduce libcll, an extensible Python toolkit for CLL research that provides a universal interface supporting various generation assumptions, datasets, and algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Complementary-label learning is a way to teach machines without giving them lots of information. Instead, you tell them what something is not, rather than what it is. This makes it harder for the machine to learn, but also more like how humans think sometimes. Some people have been trying this out, but they’ve had trouble getting good results and making it easy for others to join in. So we made a special tool called libcll that helps make it easier and more consistent. |
Keywords
» Artificial intelligence » Classification » Machine learning » Supervised
