# OASIS: Harnessing Diffusion Adversarial Network for Ocean Salinity Imputation using Sparse Drifter Trajectories

arXiv: https://arxiv.org/abs/2508.21570 · HTML: https://mlsanity.com/paper/2508.21570/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [Theoretical Limitations of Self-Attention in Neural Sequence Models](https://mlsanity.com/paper/1906.06755/) (2019)
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- [Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling](https://mlsanity.com/paper/2210.12156/) (2022)
- [Transformed Distribution Matching for Missing Value Imputation](https://mlsanity.com/paper/2302.10363/) (2023)
- [GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels](https://mlsanity.com/paper/2310.14586/) (2023)
- [Self-Supervision Improves Diffusion Models for Tabular Data Imputation](https://mlsanity.com/paper/2407.18013/) (2024)
- [PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data](https://mlsanity.com/paper/2011.07770/) (2020)
- [HexaGAN: Generative Adversarial Nets for Real World Classification](https://mlsanity.com/paper/1902.09913/) (2019)
- [Data Imputation for Sparse Radio Maps in Indoor Positioning (Extended Version)](https://mlsanity.com/paper/2302.13022/) (2023)
- [Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2012.00220/) (2020)
- [Extended Missing Data Imputation via GANs for Ranking Applications](https://mlsanity.com/paper/2011.02089/) (2020)

## Built on by (most cited first, 1 of 1)

- [Test-time GNN Model Evaluation on Dynamic Graphs](https://mlsanity.com/paper/2509.23816/) (2025)

## Abstract (arXiv)

Ocean salinity plays a vital role in circulation, climate, and marine ecosystems, yet its measurement is often sparse, irregular, and noisy, especially in drifter-based datasets. Traditional approaches, such as remote sensing and optimal interpolation, rely on linearity and stationarity, and are limited by cloud cover, sensor drift, and low satellite revisit rates. While machine learning models offer flexibility, they often fail under severe sparsity and lack principled ways to incorporate physical covariates without specialized sensors. In this paper, we introduce the OceAn Salinity Imputation System (OASIS), a novel diffusion adversarial framework designed to address these challenges.