Philipp Liznerski

"Interpretable Deep Anomaly Detection"

Anomaly detection (AD) is the task of finding anomalies in a corpus of data (e.g., find a dog among a collection of cats). Recent advances in deep learning have caused drastic improvements in this field. However, finding interpretations for these highly non-linear transformations poses a significant challenge. This talk presents several approaches to perform interpretable deep anomaly detection. We developed a detector on images that explains its decision by marking the pixels that it deems anomalous, an early anomaly detector for time series that predicts the anomalousness of future frames, and a model that detects anomalies in text while disentangling style and content.

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