Active Learning in Image Classification: A Review and Analysis

Abstract

Active Learning (AL) has emerged as a promising approach to enhance image classification tasks by intelligently selecting which data samples should be labeled for training, thereby reducing labeling costs and improving model performance. This paper provides a comprehensive review of active learning techniques applied to image classification. It discusses various AL strategies, their implementations, and their effectiveness in different scenarios. Furthermore, it analyzes the challenges and future directions in the field of active learning for image classification.