Semi-Supervised Image Regression

Semi-Supervised Image Regression is an image regression technique that combines a small amount of labeled data with a large amount of unlabeled data, aiming to improve the model's generalization and accuracy in predicting continuous numerical outputs by leveraging the structural information from the unlabeled data. This method holds significant application value in the field of computer vision, especially in scenarios where annotation costs are high or difficult to obtain, as it can effectively enhance model performance.