Summary of Allmatch: Exploiting All Unlabeled Data For Semi-supervised Learning, by Zhiyu Wu et al.
AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
by Zhiyu Wu, Jinshi Cui
First submitted to arxiv on: 22 Jun 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 This paper proposes a novel semi-supervised learning (SSL) algorithm called AllMatch, which addresses limitations in existing pseudo-labeling techniques by introducing a class-specific adaptive threshold mechanism. By leveraging the predictive confidence of the model, this approach ensures that unlabeled samples are properly labeled and utilized. The authors also design a binary classification consistency regulation strategy to distinguish candidate classes from negative options for all unlabeled data. Experimental results on multiple benchmarks show that AllMatch outperforms existing state-of-the-art methods in both balanced and imbalanced settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines learn better without needing as much labeled information. They want to make sure the machine knows what it doesn’t know, so it can use all the data available. The authors came up with a new way of doing this by looking at how confident the machine is in its predictions. This helps them decide which unlabeled data should be used to learn from. They also created a special technique to make sure they’re not wasting any data. The results show that their method, called AllMatch, works better than other methods on many types of problems. |
Keywords
» Artificial intelligence » Classification » Semi supervised
