A Novel and Efficient CVAE-GAN-Based Approach With Informative Manifold for Semi-Supervised Anomaly Detection
文献类型：期刊论文
| 作者 | Bian, Jiang1,2 ; Hui, Xiaolong1; Sun, Shiying1; Zhao, Xiaoguang1; Tan, Min1
|
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:88903-88916 |
| 关键词 | Semi-supervised anomaly detection conditional variational auto-encoder generative adversarial networks informative manifold structural similarity loss |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2019.2920251 |
| 通讯作者 | Hui, Xiaolong(huixiaolong2015@ia.ac.cn) |
| 英文摘要 | Semi-supervised anomaly detection identifies abnormal (testing) observations which are different from normal (training) observations. In many practical situations, anomalies are poorly insufficient and not well defined, while the normal data are easily sampled, have a wide variety, and may not be classified. For this paradigm, we propose a novel end-to-end deep network as an anomaly detector only trained on normal samples. Our architecture consists of a conditional variational auto-encoder (CVAE), a feature discriminator (FD), and an adversarially trained WGAN-GP discriminator. The CVAE is designed as a generator to reconstruct images. It leverages underlying category information and multivariate Gaussian distributions to regularize the latent space, enabling a smooth and informative manifold. For anomalies which have a certain similarity to normal data, we perform active negative training by generating potential outliers from the latent space to limit network generative capability. In order to capture data characteristics, we maximize the mutual information between the inputs and the latent codes by the FD. It enhances the relationship between the high-dimensional image space and corresponding encoded vectors. To promote reconstruction, a structural similarity loss is applied to robustly recover local texture details and the WGAN-GP discriminator is employed to aid in generating photo-realistic images. We distinguish anomalies by computing a reconstruction-based anomaly score. Different from recent encoder-decoder or GAN-based architectures, our approach considers input categories, constructs, and exploits a useful manifold in an unsupervised manner and has a stronger reconstruction capability. The experimental results demonstrate that the proposed approach outperforms state-of-the-art methods over several benchmark datasets. |
| WOS关键词 | FRAMEWORK ; NETWORKS |
| 资助项目 | National Natural Science Foundation of China[61421004] ; National Natural Science Foundation of China[61673378] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000476810800032 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27772]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Hui, Xiaolong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Bian, Jiang,Hui, Xiaolong,Sun, Shiying,et al. A Novel and Efficient CVAE-GAN-Based Approach With Informative Manifold for Semi-Supervised Anomaly Detection[J]. IEEE ACCESS,2019,7:88903-88916. |
| APA | Bian, Jiang,Hui, Xiaolong,Sun, Shiying,Zhao, Xiaoguang,&Tan, Min.(2019).A Novel and Efficient CVAE-GAN-Based Approach With Informative Manifold for Semi-Supervised Anomaly Detection.IEEE ACCESS,7,88903-88916. |
| MLA | Bian, Jiang,et al."A Novel and Efficient CVAE-GAN-Based Approach With Informative Manifold for Semi-Supervised Anomaly Detection".IEEE ACCESS 7(2019):88903-88916. |
入库方式： OAI收割
来源：自动化研究所
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