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A Deep One-Class Neural Network for Anomalous Event Detection in Complex Scenes.

Peng Wu, Jing Liu, Fang Shen

    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces DeepOC, a novel deep one-class classification model for anomaly detection. DeepOC effectively learns compact features and classifies anomalies in complex scenes using an adversarial mechanism.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • One-class classification is challenging for deep neural networks due to label characteristics.
    • Existing methods often rely on handcrafted features or multi-stage processes.

    Purpose of the Study:

    • To propose a novel deep one-class (DeepOC) neural network for anomaly detection.
    • To develop a generic model capable of solving one-class classification problems in complex scenarios.

    Main Methods:

    • A stacked convolutional encoder generates low-dimensional, high-level features from normal samples.
    • A decoder reconstructs raw samples to ensure feature representation diversity and correct mapping.
    • An adversarial mechanism integrates the encoder and decoder for robust feature learning and classification.

    Main Results:

    • DeepOC simultaneously learns compact feature representations and trains a one-class classifier.
    • The model achieves state-of-the-art anomaly detection results on benchmark datasets.
    • DeepOC demonstrates feasibility and effectiveness compared to existing methods.

    Conclusions:

    • DeepOC offers a robust and effective one-stage solution for anomaly detection.
    • The adversarial mechanism is key to combining feature compactness and representation diversity.
    • The model automates feature extraction, outperforming traditional and two-stage approaches.