The field of generative modeling and data imputation is rapidly advancing, with a focus on developing more efficient and effective methods for learning complex distributions. Recent work has explored the use of flow models, which transform data gradually from one modality to another, and have shown great promise in tasks such as image and tabular data generation. One of the key challenges in training flow models is the need for paired data, which can be difficult to obtain in practice. To address this, researchers have proposed using optimal transport measures to couple source and target points, leading to more efficient and effective training. Another area of research has focused on developing new methods for data imputation, including the use of conditional flow matching and guided diffusion models. These methods have shown state-of-the-art performance in a range of tasks, including missing data imputation and sea temperature reconstruction. Notable papers include:

• On Fitting Flow Models with Large Sinkhorn Couplings, which explores the benefits of using large Sinkhorn couplings in flow models.
• CFMI: Flow Matching for Missing Data Imputation, which introduces a new method for imputing missing data using conditional flow matching.