Similarity Assessment

Similarity assessment, the task of quantifying the resemblance between data points, is crucial across diverse fields, driving advancements in areas like image recognition, signature verification, and company valuation. Current research focuses on improving the explainability of similarity metrics, particularly within deep learning architectures such as Siamese networks and Vision Transformers, and exploring the impact of training data (e.g., synthetic vs. real) on model performance and internal representations. These efforts leverage various algorithms, including those based on kernel alignment, scattering wavelets, and large language models, aiming to enhance accuracy, efficiency, and fairness in applications with significant societal implications.