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Multiple Class Novelty Detection Under Data Distribution Shift

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12352)

Abstract

The novelty detection models learn a decision boundary around multiple categories of a given dataset. This helps such models in detecting any novel classes encountered during testing. However, in many cases, the test data distribution can be different from that of the training data. For such cases, the novelty detection models risk detecting a known class as novel due to the dataset distribution shift. This scenario is often ignored while working with novelty detection. To this end, we consider the problem of multiple class novelty detection under dataset distribution shift to improve the novelty detection performance. Firstly, we discuss the problem setting in detail and show how it affects the performance of current novelty detection methods. Secondly, we show that one could improve those novelty detection methods with a simple integration of domain adversarial loss. Finally, we propose a method which brings together the techniques from novelty detection and domain adaptation to improve generalization of multiple class novelty detection on different domains. We evaluate the proposed method on digits and object recognition datasets and show that it provides improvements over the baseline methods.

Keywords

Dataset distribution shift Multiple class novelty detection 

Notes

Acknowledgement

This work was supported by the NSF grant 1910141.

Supplementary material

504444_1_En_26_MOESM1_ESM.pdf (1006 kb)
Supplementary material 1 (pdf 1006 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Johns Hopkins UniversityBaltimoreUSA
  2. 2.University of HoustonHoustonUSA

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