Summary of One-shot Active Learning Based on Lewis Weight Sampling For Multiple Deep Models, by Sheng-jun Huang et al.
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models
by Sheng-Jun Huang, Yi Li, Yiming Sun, Ying-Peng Tang
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 active learning (AL) approach that enables efficient training of multiple target models while minimizing labeled data querying. The proposed one-shot AL method avoids the need for iterative model training, making it particularly suitable for deep models that can be computationally expensive to train. By leveraging this method, researchers and practitioners can reduce the cost and time required to develop and train multiple models concurrently. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about finding a better way to teach machines to learn from a small amount of labeled data while training many different models at once. The current methods are too slow and use too much computing power. The new approach, called one-shot AL, does all the learning in just one go, without needing to train the model multiple times. This will make it easier and faster for people to develop lots of different machine learning models. |
Keywords
» Artificial intelligence » Active learning » Machine learning » One shot
