Dynamic Class-Aware Selection in Active Learning for Class-Imbalanced Semi-Supervised Learning

Abstract

The performance of machine learning classification tasks heavily depends on both the availability and quality of labeled data. This challenge is particularly significant in industrial fields dominated by non-natural images due to costly expert annotation. Moreover, non-natural image datasets often exhibit severe class-wise imbalance. Active learning (AL) has emerged to improve labeled data quality by selecting informative samples within the same annotation budget. While some methods address imbalance, they fall short in scenarios with limited annotation budget, leaving a critical gap in tackling both limited availability and imbalance of labeled data for practical industrial applications. To tackle this issue, we proposed a dynamic selection mechanism based on a class-aware ranking strategy within the AL process, aiming to select more balanced labeled data under limited annotation budget. Furthermore, we incorporated this mechanism into a two-stage semi-supervised learning (SSL) framework. We evaluated the proposed method on two non-natural image datasets for image classification tasks. Results showed that our method achieved the highest F1 scores, effectively addressing both imbalance and limited labeled data challenges in non-natural image classification.