Summary of Lego-learn: Label-efficient Graph Open-set Learning, by Haoyan Xu et al.
LEGO-Learn: Label-Efficient Graph Open-Set Learning
by Haoyan Xu, Kay Liu, Zhengtao Yao, Philip S. Yu, Kaize Ding, Yue Zhao
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 This paper tackles the problem of training graph-based models that can recognize new classes without requiring a massive amount of labeled data. Specifically, it focuses on open-set learning (GOL) and out-of-distribution (OOD) detection, which aim to classify known classes while identifying and handling unseen classes during inference. The significance of this work lies in its potential to improve high-stakes applications like finance, security, and healthcare where models may encounter unexpected data. Current GOL methods rely on having many labeled samples for known classes, but this assumption is unrealistic for large-scale graphs due to the high cost of annotation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us train graph-based models that can recognize new things without needing lots of labeled information. It’s all about learning how to tell what we know from what we don’t know. This is important because in real-life situations, like finance or healthcare, we need our models to be able to handle unexpected data. Right now, the way we do this requires too much labeled information, which can be very expensive. |
Keywords
» Artificial intelligence » Inference
