Summary of Dcom: Active Learning For All Learners, by Inbal Mishal and Daphna Weinshall
DCoM: Active Learning for All Learners
by Inbal Mishal, Daphna Weinshall
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 The proposed Deep Active Learning (AL) technique, Dynamic Coverage & Margin mix (DCoM), aims to bridge the gap between optimal results in varying budget scenarios. Unlike existing strategies, DCoM dynamically adjusts its approach based on the current model’s competence. This novel AL approach achieves state-of-the-art performance across both low- and high-budget regimes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new active learning technique called Dynamic Coverage & Margin mix (DCoM) that can be used to train deep models while reducing annotation costs. DCoM is designed to work well in different budget scenarios, unlike other existing strategies. The authors tested their approach on several datasets and found that it performed well in both low- and high-budget regimes. |
Keywords
* Artificial intelligence * Active learning
