Consistency Based Semi Supervised Learning

Consistency-based semi-supervised learning aims to improve model performance by leveraging unlabeled data alongside limited labeled data, addressing the scarcity of annotated datasets in many applications. Current research focuses on developing methods that enforce consistency in model predictions across different data augmentations or views, often employing techniques like pseudo-labeling, adversarial training, or contrastive learning within various architectures, including transformers and convolutional neural networks. This approach is proving valuable across diverse fields, from medical image analysis and activity recognition to anomaly detection and image manipulation detection, enabling more accurate and efficient models with reduced reliance on extensive manual labeling.