# Understanding Ice Crystal Habit Diversity with Self-Supervised Learning

arXiv: https://arxiv.org/abs/2509.07688 · HTML: https://mlsanity.com/paper/2509.07688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Masked Siamese Networks for Label-Efficient Learning](https://mlsanity.com/paper/2204.07141/) (2022)
- [Revisiting Self-Supervised Visual Representation Learning](https://mlsanity.com/paper/1901.09005/) (2019)
- [Self-Supervised Representation Learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2110.09327/) (2021)
- [Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach](https://mlsanity.com/paper/2405.15613/) (2024)
- [DINO as a von Mises-Fisher mixture model](https://mlsanity.com/paper/2405.10939/) (2024)
- [On Pretraining Data Diversity for Self-Supervised Learning](https://mlsanity.com/paper/2403.13808/) (2024)
- [On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods](https://mlsanity.com/paper/2410.14060/) (2024)

## Abstract (arXiv)

Ice-containing clouds strongly impact climate, but they are hard to model due to ice crystal habit (i.e., shape) diversity. We use self-supervised learning (SSL) to learn latent representations of crystals from ice crystal imagery. By pre-training a vision transformer with many cloud particle images, we learn robust representations of crystal morphology, which can be used for various science-driven tasks. Our key contributions include (1) validating that our SSL approach can be used to learn meaningful representations, and (2) presenting a relevant application where we quantify ice crystal diversity with these latent representations. Our results demonstrate the power of SSL-driven representations to improve the characterization of ice crystals and subsequently constrain their role in Earth's climate system.