2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDARS-6.2
Paper Title SAGAN: SKIP-ATTENTION GAN FOR ANOMALY DETECTION
Authors Guoliang Liu, Shiyong Lan, Ting Zhang, Weikang Huang, Sichuan University, China; Wenwu Wang, University of Surrey, United Kingdom
SessionARS-6: Image and Video Interpretation and Understanding 1
LocationArea H
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Analysis, Synthesis, and Retrieval: Image & Video Interpretation and Understanding
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Generative Adversarial Networks (GANs) have been used recently for anomaly detection from images, where the anomaly scores are obtained by comparing the global difference between the input and generated image. However, the anomalies often appear in local areas of an image scene, and ignoring such information can lead to unreliable detection of anomalies. In this paper, we propose an efficient anomaly detection network Skip-Attention GAN (SAGAN), which adds attention modules to capture local information to improve the accuracy of latent representation of images, and uses depth-wise separable convolutions to reduce the number of parameters in the model. We evaluate the proposed method on the CIFAR-10 dataset and the LBOT dataset (built by ourselves), and show that the performance of our method in terms of area under curve (AUC) on both datasets is improved by more than 10% on average, as compared with three recent baseline methods.