Semi-Supervised Image Classification

Semi-supervised image classification is a technique that combines labeled and unlabeled data to improve classification performance. This method enhances the model's generalization and accuracy by leveraging a large amount of unannotated images, effectively alleviating the issue of insufficient labeled data, and has significant application value in the field of computer vision.