Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
A contrastive semi-supervised learning (CSSL) algorithm for ice crystal classification that reduces manual labeling effort by 90% (154 hours saved) while maintaining high accuracy, achieving 89.6% accuracy with only 25% of labeled data.
Jun 27, 2025
