Summary of Robust Semi-supervised Learning in Open Environments, by Lan-zhe Guo et al.
Robust Semi-Supervised Learning in Open Environments
by Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao, Yu-Feng Li
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Semi-supervised learning (SSL) aims to leverage unlabeled data when labels are scarce, improving performance by exploiting this abundant resource. However, conventional studies assume consistent environments where factors like label, feature, and distribution between labeled and unlabeled data remain the same. In contrast, real-world tasks involve open environments with inconsistent factors, leading to severe performance degradation, even worse than simple supervised learning. To overcome these limitations, robust SSL methods that can handle inconsistent unlabeled data are essential. This paper explores advances in this area, focusing on techniques addressing label, feature, and distribution inconsistency in SSL, as well as presenting evaluation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised learning is a way to use extra information when we don’t have enough labeled data. Normally, researchers assume that the data looks similar when there are labels or not. But sometimes, this isn’t true. When it’s not true, using extra data can actually make things worse than just using the labeled data alone. To fix this problem, scientists are working on ways to use extra information even when the data is different. This paper talks about some of these new ideas and how they’re tested. |
Keywords
» Artificial intelligence » Semi supervised » Supervised
