Wasserstein Dictionary

Wasserstein dictionaries leverage the Wasserstein distance, a metric measuring the optimal transport between probability distributions, to represent and analyze complex data. Current research focuses on applying this framework to diverse problems, including generative modeling, adversarial training, and clustering of probability distributions, often employing techniques like Wasserstein barycenters and adversarial networks within novel model architectures. This approach offers improved robustness, enhanced performance in handling uncertainty and imbalanced data, and provides a powerful tool for various applications ranging from credit risk assessment to open-set object detection and reinforcement learning.