Dataset Dictionary Learning

Dataset dictionary learning addresses the challenge of multi-source domain adaptation in machine learning, aiming to effectively transfer knowledge from multiple source datasets to a target dataset with differing distributions. Current research focuses on developing decentralized and federated learning approaches using Wasserstein barycenters and Gaussian Mixture Models to represent data distributions and learn shared dictionary atoms, thereby preserving data privacy while improving adaptation performance. This methodology shows promise for enhancing the robustness and efficiency of transfer learning across diverse applications, particularly in scenarios with limited data or privacy constraints.