Unsupervised anomaly detection (UAD) is a diverse research area explored across various
application domains. Over time, numerous anomaly detection techniques, including …

Many variants of the Wasserstein distance have been introduced to reduce its original
computational burden. In particular the Sliced-Wasserstein distance (SW), which leverages …

The sliced Wasserstein (SW) distance has been widely recognized as a statistically effective
and computationally efficient metric between two probability measures. A key component of …

Sliced Wasserstein (SW) distance has been widely used in different application scenarios
since it can be scaled to a large number of supports without suffering from the curse of …

The conventional sliced Wasserstein is defined between two probability measures that have
realizations as\textit {vectors}. When comparing two probability measures over images …

Abstract Max sliced Wasserstein (Max-SW) distance has been widely known as a solution
for less discriminative projections of sliced Wasserstein (SW) distance. In applications that …

Sliced Wasserstein (SW) distance suffers from redundant projections due to independent
uniform random projecting directions. To partially overcome the issue, max K sliced …

Comparing spherical probability distributions is of great interest in various fields, including
geology, medical domains, computer vision, and deep representation learning. The utility of …

Slicing distribution selection has been used as an effective technique to improve the
performance of parameter estimators based on minimizing sliced Wasserstein distance in …

The sliced Wasserstein (SW) distances between two probability measures are defined as
the expectation of the Wasserstein distance between two one-dimensional projections of the …