Computer Vision Transduction

Transductive learning in computer vision tasks refers to a learning method that uses both known labeled data and unlabeled data directly for prediction. Its goal is to improve the model's generalization ability and prediction accuracy, especially when labeled data is limited. By optimizing the representation of unlabeled data, this method can play a significant role in tasks such as image classification and object detection, enhancing the practical value of the model.