Semi Supervised Image Classification

Semi-supervised image classification aims to improve the accuracy of image classifiers by leveraging both labeled and unlabeled data, reducing the need for extensive manual annotation. Current research focuses on refining pseudo-labeling techniques, exploring novel loss functions (e.g., contrastive loss, entropy-based methods), and adapting various architectures, including transformers and graph convolutional networks, to effectively utilize unlabeled data. These advancements are significant because they enable the development of more accurate and efficient image classification models, particularly in domains with limited labeled data, impacting fields like medical imaging, remote sensing, and video surveillance.