2 papers
math.ST2026
Quantifying the noise sensitivity of the Wasserstein metric for images
Erik Lager, Gilles Mordant, Amit Moscovich
Wasserstein metrics are increasingly adopted as similarity scores for images. We consider the sensitivity of Wasserstein metrics with respect to pixel-wise additive noise when the…
cs.LG2025
Quantization-based Bounds on the Wasserstein Metric
Jonathan Bobrutsky, Amit Moscovich
The Wasserstein metric has become increasingly important in many machine learning applications such as generative modeling, image retrieval and domain adaptation. Despite its appea…