Improved Algorithm for Deep Active Learning under Imbalance via Optimal Separation

Understand

Class imbalance severely impacts machine learning performance on minority classes in real-world applications.

• While various solutions exist, active learning offers a fundamental fix by strategically collecting balanced, informative labeled examples from abundant unlabeled data.
• We introduce DIRECT, an algorithm that identifies class separation boundaries and selects the most uncertain nearby examples for annotation.
• By reducing the problem to one-dimensional active learning, DIRECT leverages established theory to handle batch labeling and label noise -- another common challenge in data annotation that particularly affects active learning methods.