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Fast Distance-Based Anomaly Detection in Images Using an Inception-Like Autoencoder

  • Natasa Sarafijanovic-DjukicEmail author
  • Jesse Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

Abstract

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, using normal examples, a convolutional autoencoder (CAE) is trained to extract a low-dimensional representation of the images. Here, we propose a novel architectural choice when designing the CAE, an Inception-like CAE. It combines convolutional filters of different kernel sizes and it uses a Global Average Pooling (GAP) operation to extract the representations from the CAE’s bottleneck layer. Second, we employ a distanced-based anomaly detector in the low-dimensional space of the learned representation for the images. However, instead of computing the exact distance, we compute an approximate distance using product quantization. This alleviates the high memory and prediction time costs of distance-based anomaly detectors. We compare our proposed approach to a number of baselines and state-of-the-art methods on four image datasets, and we find that our approach resulted in improved predictive performance.

Keywords

Anomaly detection Deep learning Computer vision 

Notes

Acknowledgements

We thank Lukas Ruff from TU Berlin for help reproducing the results from [25]. This research has been partially funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 752907. JD is partially supported by KU Leuven Research Fund (C14/17/07, C32/17/036), Research Foundation - Flanders (EOS No. 30992574, G0D8819N), VLAIO-SBO grant HYMOP (150033), and the Flanders AI Impulse Program.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IRIS Technology SolutionsBarcelonaSpain
  2. 2.KU LeuvenLeuvenBelgium

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