This article explains how active learning can dramatically reduce the amount of labeled data required for training deep neural networks by selecting the most informative and representative samples, and provides a complete Python implementation of a hybrid query strategy (DBAL) with ResNet‑18.
Active learning addresses the high cost of labeling data by iteratively selecting the most informative unlabeled samples for annotation, thereby reducing labeling effort while achieving target model performance, and the article explains its fundamentals, relationship to supervised and semi‑supervised learning, common selection strategies, hybrid methods, and evaluation metrics.
Active Learning is a machine learning approach that reduces labeling costs by iteratively selecting the most informative samples for human annotation, using various query strategies such as uncertainty sampling, query-by-committee, expected model change, and density-weighted methods, applicable to domains like image classification, security risk control, and anomaly detection.
