English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a new method of training energy-based models (EBMs) for anomalydetection that leverages low-dimensional structures within data. The proposedalgorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a datapoint along a low-dimensional manifold that approximates the training dataset.Then, EBM is trained to maximize the probability of recovering the originaldata. The training involves the generation of negative samples via MCMC, as inconventional EBM training, but from a different distribution concentrated nearthe manifold. The resulting near-manifold negative samples are highlyinformative, reflecting relevant modes of variation in data. An energy functionof MPDR effectively learns accurate boundaries of the training datadistribution and excels at detecting out-of-distribution samples. Experimentalresults show that MPDR exhibits strong performance across various anomalydetection tasks involving diverse data types, such as images, vectors, andacoustic signals.

Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach