Soft Contrastive Representation Learning for Cloud-Particle Images Captured In-Flight by the New HVPS-4 Airborne Probe
Bibliographic record
Abstract
Cloud properties underpin accurate climate modeling and are often derived from the individual particles comprising a cloud. Studying these cloud particles is challenging due to their intricate shapes, called “habits,” and manual classification via probe-generated images is time-consuming and subjective. We propose a novel method for habit representation learning that uses minimal labeled data by leveraging self-supervised learning (SSL) with Vision Transformers (ViTs) on a newly acquired dataset of 124000 images captured by the novel high-volume precipitation spectrometer ver. 4 (HVPS-4) probe. Our approach significantly outperforms ImageNet pretraining by 48% on a 293-sample annotated dataset. Notably, we present the first SSL scheme for learning habit representations, leveraging data collected in flight from the probe. Our results demonstrate that self-supervised pretraining significantly improves habit classification even when using single-channel HVPS-4 data. We achieve further gains using sequential views and a soft contrastive objective tailored for sequential, in-flight measurements. Our work paves the way for applying SSL to multiview and multiscale data from advanced cloud-particle imaging probes, enabling comprehensive characterization of the flight environment. We publicly release data, code, and models associated with this study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".