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

Generative models trained in an unsupervised manner may set high likelihoodand low reconstruction loss to Out-of-Distribution (OoD) samples. Thisincreases Type II errors and leads to missed anomalies, overall decreasingAnomaly Detection (AD) performance. In addition, AD models underperform due tothe rarity of anomalies. To address these limitations, we propose the OoDMinimum Anomaly Score GAN (OMASGAN). OMASGAN generates, in a negative dataaugmentation manner, anomalous samples on the estimated distribution boundary.These samples are then used to refine an AD model, leading to more accurateestimation of the underlying data distribution including multimodal supportswith disconnected modes. OMASGAN performs retraining by including the abnormalminimum-anomaly-score OoD samples generated on the distribution boundary in aself-supervised learning manner. For inference, for AD, we devise adiscriminator which is trained with negative and positive samples eithergenerated (negative or positive) or real (only positive). OMASGAN addresses therarity of anomalies by generating strong and adversarial OoD samples on thedistribution boundary using only normal class data, effectively addressing modecollapse. A key characteristic of our model is that it uses any f-divergencedistribution metric in its variational representation, not requiringinvertibility. OMASGAN does not use feature engineering and makes noassumptions about the data distribution. The evaluation of OMASGAN on imagedata using the leave-one-out methodology shows that it achieves an improvementof at least 0.24 and 0.07 points in AUROC on average on the MNIST and CIFAR-10datasets, respectively, over other benchmark and state-of-the-art models forAD.

OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary