Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation
- URL: http://arxiv.org/abs/2103.16851v1
- Date: Wed, 31 Mar 2021 07:04:07 GMT
- Title: Attention Map-guided Two-stage Anomaly Detection using Hard Augmentation
- Authors: Jou Won Song, Kyeongbo Kong, Ye In Park, Suk-Ju Kang
- Abstract summary: Anomaly detection is a task that recognizes whether an input sample is included in the distribution of a target normal class or an anomaly class.
This paper proposes a novel two-stage network consisting of an attention network and an anomaly detection GAN.
- Score: 12.272975892517039
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Anomaly detection is a task that recognizes whether an input sample is
included in the distribution of a target normal class or an anomaly class.
Conventional generative adversarial network (GAN)-based methods utilize an
entire image including foreground and background as an input. However, in these
methods, a useless region unrelated to the normal class (e.g., unrelated
background) is learned as normal class distribution, thereby leading to false
detection. To alleviate this problem, this paper proposes a novel two-stage
network consisting of an attention network and an anomaly detection GAN
(ADGAN). The attention network generates an attention map that can indicate the
region representing the normal class distribution. To generate an accurate
attention map, we propose the attention loss and the adversarial anomaly loss
based on synthetic anomaly samples generated from hard augmentation. By
applying the attention map to an image feature map, ADGAN learns the normal
class distribution from which the useless region is removed, and it is possible
to greatly reduce the problem difficulty of the anomaly detection task.
Additionally, the estimated attention map can be used for anomaly segmentation
because it can distinguish between normal and anomaly regions. As a result, the
proposed method outperforms the state-of-the-art anomaly detection and anomaly
segmentation methods for widely used datasets.
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