Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 12
Articles | Volume 18, issue 12
https://doi.org/10.5194/amt-18-2781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2781-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 12
Research article
 | 
27 Jun 2025
Research article |  | 27 Jun 2025

Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm

Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger

Viewed

Total article views: 673 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
533 99 41 673 26 34
  • HTML: 533
  • PDF: 99
  • XML: 41
  • Total: 673
  • BibTeX: 26
  • EndNote: 34
Views and downloads (calculated since 13 Nov 2024)
Cumulative views and downloads (calculated since 13 Nov 2024)

Viewed (geographical distribution)

Total article views: 673 (including HTML, PDF, and XML) Thereof 673 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Jul 2025
Download
Short summary
Our study improves ice crystal shape classification, key for understanding weather and climate. By adding unsupervised pre-training before supervised classification, our algorithm reduces manual labeling effort while maintaining high accuracy. It outperforms fully supervised models across datasets of varying sizes and categories, showing strong generalization ability. This method improves ice crystal classification techniques, making it adaptable to different environmental datasets.
Read more
Share