Semi Supervised Active Learning

Semi-supervised active learning (SSAL) aims to efficiently train machine learning models by strategically selecting a small subset of unlabeled data for human annotation, while simultaneously leveraging the information contained within the larger pool of unlabeled data. Current research focuses on improving the selection of informative samples through techniques like ranking-based loss prediction and sensor consistency analysis, often incorporating semi-supervised learning methods such as pseudo-labeling and adversarial training within various model architectures, including variational autoencoders and ensemble methods. This approach is significant because it reduces the substantial cost and time associated with data annotation, making advanced machine learning techniques more accessible for applications like image classification, video action detection, and semantic segmentation, particularly in resource-constrained settings.