Summary of Deep Active Learning: a Reality Check, by Edrina Gashi et al.
Deep Active Learning: A Reality Check
by Edrina Gashi, Jiankang Deng, Ismail Elezi
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 A comprehensive evaluation of state-of-the-art deep active learning methods reveals that no single-model method consistently outperforms entropy-based active learning, with some even falling short of random sampling. The study delves into overlooked aspects such as starting budget, budget step, and pretraining’s impact, highlighting their significance in achieving superior results. Additionally, the evaluation extends to other tasks, including semi-supervised learning and object detection, providing valuable insights and concrete recommendations for future active learning studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models can be trained more efficiently with limited annotation budgets using active learning methods. Researchers evaluate various deep active learning methods and find that entropy-based active learning performs well under general settings. The study also explores the impact of starting budget, budget step, and pretraining on active learning effectiveness. By understanding these limitations and experimental settings, researchers can make informed decisions when applying active learning to their tasks. |
Keywords
* Artificial intelligence * Active learning * Deep learning * Object detection * Pretraining * Semi supervised
