.png){kind=icon}
返回
Improved Autoencoder Model With Memory Module for Anomaly Detection
.png){kind=icon}
PRE
AI
DOI：10.1109/JSEN.2024.3370965.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As a commonly used model for anomaly detection, the autoencoder model for anomaly detection does not train the objective for extracted features, which is a downside of autoencoder model. In addition, it is well known that the autoencoder model has over-prominent reconstruction ability for anomalous data, leading to high false-negative rate. On the other hand, the deep support vector data description (SVDD) model first extracts features through deep neural network, and then map the extracted features into a hypersphere. However, the deep SVDD model has disadvantages such as feature information loss and feature collapse during training process, leading to a decrease in anomaly detection accuracy. To alleviate such drawbacks mentioned above, in this article, we propose a novel model, called improved autoencoder with memory module (IAEMM). Specifically, this model jointly learns deep SVDD model and autoencoder model to minimize the overall loss of deep SVDD error and reconstruction error, and add a memory module after encoder to amplify the difference of reconstruction error between normal and abnormal data. The proposed model can well identify abnormal hidden features and mitigate the problem of feature collapse. Experimental results on several datasets confirm the effectiveness and stability of our proposed method.
Keyword:
Anomaly detection
autoencoder
deep support vector data description (SVDD)
memory
期刊
IF:
4.5
论文数:
2.2W
被引数:
7.3W
机构
引用论文
Antioxidant activities and bioactive compounds of five Jalopeno peppers (Capsicum annuum) cultivars
PREAI
分享收藏分享收藏Comprehensive Analysis of Expressed Proteins in the Different Stages of the Style Development of Self-incompatible ‘Hyuganatsu’ (Citrus tamurana hort. ex Tanaka)‘Hyuganatsu’（Citrus tamurana hort. ex Tanaka）自交不亲和性柱头发育不同阶段的表达蛋白质综合分析
OAAI
OAAI分享收藏Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder
PREAI
分享收藏
Memory Clustering Autoencoder Method for Human Action Anomaly Detection on Surveillance Camera Video.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏分享收藏
查看更多内容