Subject Information Extraction for Novelty Detection with Domain Shifts

• URL: http://arxiv.org/abs/2504.21247v1
• Date: Wed, 30 Apr 2025 01:04:55 GMT
• Title: Subject Information Extraction for Novelty Detection with Domain Shifts
• Authors: Yangyang Qu, Dazhi Fu, Jicong Fan,
• Abstract summary: Unsupervised novelty detection (UND) is essential in fields like medical diagnosis, cybersecurity, and industrial quality control.<n>Most existing UND methods assume that the training data and testing normal data originate from the same domain.<n>In real scenarios, it is common for normal testing and training data to originate from different domains, a challenge known as domain shift.
• Score: 20.89386559615201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised novelty detection (UND), aimed at identifying novel samples, is essential in fields like medical diagnosis, cybersecurity, and industrial quality control. Most existing UND methods assume that the training data and testing normal data originate from the same domain and only consider the distribution variation between training data and testing data. However, in real scenarios, it is common for normal testing and training data to originate from different domains, a challenge known as domain shift. The discrepancies between training and testing data often lead to incorrect classification of normal data as novel by existing methods. A typical situation is that testing normal data and training data describe the same subject, yet they differ in the background conditions. To address this problem, we introduce a novel method that separates subject information from background variation encapsulating the domain information to enhance detection performance under domain shifts. The proposed method minimizes the mutual information between the representations of the subject and background while modelling the background variation using a deep Gaussian mixture model, where the novelty detection is conducted on the subject representations solely and hence is not affected by the variation of domains. Extensive experiments demonstrate that our model generalizes effectively to unseen domains and significantly outperforms baseline methods, especially under substantial domain shifts between training and testing data.

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