30 citations · 33 across the 5 of their papers we have counts for
5 papers
Unsupervised Anomaly Detection with Rejection
Lorenzo Perini, Jesse Davis
Anomaly detection aims at detecting unexpected behaviours in the data. Because anomaly detection is usually an unsupervised task, traditional anomaly detectors learn a decision bou…
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection
Lorenzo Perini, Daniele Giannuzzi, Jesse Davis
Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous la…
Adversarial Example Detection in Deployed Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a mispredict…
Elastic Product Quantization for Time Series
Pieter Robberechts, Wannes Meert, Jesse Davis
Analyzing numerous or long time series is difficult in practice due to the high storage costs and computational requirements. Therefore, techniques have been proposed to generate c…
Fast Distance-based Anomaly Detection in Images Using an Inception-like Autoencoder
Natasa Sarafijanovic-Djukic, Jesse Davis
The goal of anomaly detection is to identify examples that deviate from normal or expected behavior. We tackle this problem for images. We consider a two-phase approach. First, usi…