AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets (2024.naacl-long)
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| Challenge: | Standard pool-based active learning is computationally expensive on large pools and often reaches low accuracy by overfitting the initial decision boundary. |
| Approach: | They propose a pool-based active learning method that selects class-specific instances from a labelled set and retrieves the most similar unlabelled instances from the pool. |
| Outcome: | Experiments with AnchorAL show that it is faster, often reducing runtime from hours to minutes, and trains more performant models. |
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