UNSUPERVISED IMAGE-BASED ANOMALY DETECTION USING MULTI-SCALE CONTEXT-DEPENDENT DEEP AUTOENCODING GAUSSIAN MIXTURE MODEL

• 1. 一种基于深度自编码器高斯混合模型的用电异常检测方法 [P] . 中国专利： CN113902581A . 2022-01-07
• 2. 一种基于上下文的无监督关系数据异常检测方法 [P] . 中国专利： CN108038211A . 2018-05-15
• 3. SATELLITE SEA-FOG DETECTION SYSTEM USING THE GAUSSIAN MIXTURE MODE, AND FOG DETECTION METHOD WITH UNSUPERVISED LEARNING MODE BASED ON GAUSSIAN MIXTURE MODEL [P] . 韩国专利： KR20160019788A . 2016-02-22

  机译： 高斯混合模型的卫星海雾检测系统及基于高斯混合模型的无监督学习模式的雾检测方法

• 4. Satellite sea-fog detection system using the gaussian mixture mode and fog detection method with unSupervised Learning mode based on gaussian mixture model [P] . 韩国专利： KR101620068B1 . 2016-05-11

  机译： 基于高斯混合模型的高斯混合模式和无监督学习雾检测方法的卫星海雾检测系统

• 5. UNSUPERVISED ADAPTATION AND CLASSIFICATION OF MULTI-SOURCE DATA USING A GENERALIZED GAUSSIAN MIXTURE MODEL [P] . 欧洲知识产权局专利： EP1247252A4 . 2007-03-28

  机译： 使用广义高斯混合模型对多源数据进行未经监督的自适应和分类

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• 7. Anomaly Detection in Videos Through Deep Unsupervised Techniques [D] . Shembekar, Parikshit Prashant. 2020
• 8. FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models [O] . Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Ali Chattha, 2019
• 9. Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images [O] . Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, 2019

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