Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
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Deep anomaly detection has proven to be an efficient and robust approach in several fields. The introduction of self-supervised learning has greatly helped many methods including anomaly detection where simple geometric transformation recognition tasks are used. However these methods do not perform well on fine-grained problems since they lack finer features and are usually highly dependent on the anomaly type. In this paper, we explore each step of self-supervised anomaly detection with pretext tasks. First, we introduce novel discriminative and generative tasks which focus on different visual cues. A piece-wise jigsaw puzzle task focuses on structure cues, while a tint rotation recognition is used on each piece for colorimetry and a partial re-colorization task is performed. In order for the re-colorization task to focus more on the object rather than on the background, we propose to include the contextual color information of the image border. Then, we present a new out-of-distribution detection function and highlight its better stability compared to other out-of-distribution detection methods. Along with it, we also experiment different score fusion functions. Finally, we evaluate our method on a comprehensive anomaly detection protocol composed of object anomalies with classical object recognition, style anomalies with fine-grained classification and local anomalies with face anti-spoofing datasets. Our model can more accurately learn highly discriminative features using these self-supervised tasks. It outperforms state-of-the-art with up to 36 improvement on object anomalies and 40
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