A Comprehensive Guide to Semi-Supervised Learning in Computer Vision: Algorithms, Comparisons, and Techniques
Introduction to Semi-Supervised Learning Semi-Supervised Learning is a deep learning technique that utilizes a small amount of labeled data and a large amount of unlabeled data. Traditional Supervised Learning uses only labeled data for training, but acquiring labeled data is often difficult and time-consuming. In contrast, Semi-Supervised Learning improves model performance by utilizing unlabeled data, achieving better results with less labeling effort in real-world scenarios. This approach is particularly advantageous in computer vision tasks such as image classification, object detection, and video analysis. When there is a lack of labeled data in large-scale image datasets, Semi-Supervised Learning can effectively enhance model performance using unlabeled data. Technical Background: The core techniques of Semi-Supervised Learning are Consistency Regularization and Pseudo-labeling . Consistency Regularization encourages the model to make consistent predictions on au...