Dec 11, 2018 · Outlier Exposure significantly improves anomaly detection performance in natural language processing and vision tasks, especially for unseen ...
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Dec 20, 2018 · Outlier Exposure improves anomaly detection performance by generalizing and detecting unseen anomalies in natural language processing and ...
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Outlier Exposure (OE) improves anomaly detection performance in deep learning models by fine-tuning a classifier to learn heuristics for distinguishing anomalies from in-distribution samples.
Outlier Exposure (OE) improves deep anomaly detection by training anomaly detectors against an auxiliary dataset of outliers, which generalizes and detects unseen anomalies.
Outlier Exposure improves anomaly detection performance by generalizing to unseen anomalies in NLP and vision tasks.
This enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments on natural language processing and small- and large-scale ...
Sep 8, 2024 · This approach enables anomaly detectors to generalize and detect unseen anomalies. In extensive experiments in vision and natural language ...
▷ Biometric authentication, etc. Such irrelevant samples are called out-of-distribution (OOD) samples. The goal is to detect them.
In extensive experiments on natural language processing and small- and large-scale vision tasks, it is found that Outlier Exposure significantly improves ...
Jul 25, 2019 · Bibliographic details on Deep Anomaly Detection with Outlier Exposure.