Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
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by
Loic Jezequel, Ngoc-Son Vu, Jean Beaudet, Aymeric Histace
2021
Abstract
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% relative error
improvement on object anomalies and 40% on face anti-spoofing problems.
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