Detecting the presence of outliers, like novelties or anomalies, with respect to a particular distribution has numerous applications in computer vision and in nearly every area of data science. In (Pidhorskyi et al., 2018) we introduced a generative based approach that aims at learning the manifold of the inliers, and that efficiently computes the likelihood of a data point to have been drawn from that distribution. The manifold learning can be problematic for high dimensional data. On the one hand specialized architectures are needed, like autoencoders (Pidhorskyi et al., 2020), on the other hand, it is important to leverage the local geometry of the manifold to improve the computational efficiency (Almohsen et al., 2022).
Stanislav Pidhorskyi, Ranya Almohsen, Donald A Adjeroh, Gianfranco Doretto
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