Summary of Querying Easily Flip-flopped Samples For Deep Active Learning, by Seong Jin Cho et al.
Querying Easily Flip-flopped Samples for Deep Active Learning
by Seong Jin Cho, Gwangsu Kim, Junghyun Lee, Jinwoo Shin, Chang D. Yoo
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 approach to active learning, a machine learning paradigm that improves model performance by selectively querying unlabeled data. The key innovation is the “least disagree metric” (LDM), which measures predictive uncertainty by calculating the smallest probability of disagreement between predicted labels and actual classes. An efficient estimator for LDM is developed, which can be easily integrated into deep learning models using parameter perturbation. Experimental results demonstrate state-of-the-art overall performance on various datasets and architectures when using LDM-based active learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active learning helps machines learn from data by choosing what to learn next. A new way to do this is proposed in this paper, which uses a “least disagree metric” (LDM) to decide what’s most important to learn. The LDM is like a measure of how certain the machine is about its answers. The researchers developed an easy way to calculate this metric and used it to test their approach on different datasets and machines. It worked really well! |
Keywords
* Artificial intelligence * Active learning * Deep learning * Machine learning * Probability
