Summary of Annotation Efficiency: Identifying Hard Samples Via Blocked Sparse Linear Bandits, by Adit Jain et al.
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits
by Adit Jain, Soumyabrata Pal, Sunav Choudhary, Ramasuri Narayanam, Vikram Krishnamurthy
First submitted to arxiv on: 26 Oct 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 addresses the challenge of annotating datapoints with limited annotation rounds in a scenario where labels are scarce. It proposes soliciting feedback from experts on the difficulty of annotating each datapoint, in addition to ground truth labels. The authors find that existing active learning and coreset selection methods are not applicable in this setting since they rely on the presence of a reliable trained model, which is absent in the label-scarce regime. Instead, they model the sequential decision-making problem as a sparse linear bandit framework with a blocking constraint. They propose an algorithm called BSLB that achieves a regret guarantee and demonstrate its effectiveness for annotation in the label-scarce setting using real-world data from the PASCAL-VOC dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us figure out how to ask experts to help label data when we don’t have many labels. They come up with a new way to do this by asking the expert how hard it is to label each piece of data. This helps us make better choices about which pieces of data to label first. The authors show that their method works well for labeling images, even when we don’t have many labels. |
Keywords
» Artificial intelligence » Active learning
