Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection 文章链接 https://paperswithcode.com/search?q_meta=&q=Memorizing+Normality+to+Detect+Anomaly%3A+Memory-augmented+Deep+Autoencoder+for+Unsupervised+Anomaly+Detection 背景介绍 无监督异常检测:利用正常...
深度自编码器(Deep autoencoder, AE)是一种强大的工具,可以对无监督设置下的高维数据进行建模。它由编码器和解码器组成,前者用于从输入中获取压缩编码,后者用于从编码中重构数据。编码实质上是迫使网络提取高维数据典型模式的信息瓶颈。在异常检测的背景下,声发射通常通过对正常数据进行重构误差最小化训练,然后将重构...
值得注意的是,当数据点位于高维空间(即视频)中时,问题变得更加困难,因为对高维数据建模是出了名的具有挑战性的。深度自编码器(Deep autoencoder, AE)是一种强大的工具,可以对无监督设置下的高维数据进行建模。它由编码器和解码器组成,前者用于从输入中获取压缩编码,后者用于从编码中重构数据。编码实质上是迫使网络...
This is an unofficial implementation of paper "Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection". Majority of the code are based on the original repohttps://github.com/donggong1/memae-anomaly-detection ...
《Multi-Facet Clustering Variational Autoencoders》(NeurIPS 2021) GitHub: github.com/FabianFalck/mfcvae [fig9]《Fast Light-field Disparity Estimation with Multi-disparity-scale Cost Aggregation》(ICCV 2021) GitHub: github.com/zcong17huang/FastLFnet [fig10]...
Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel. In IEEE International Conference on Computer Vision (ICCV), 2019. [...
The overall framework consists of a deep autoencoder, a memory module with an attention-based addressing operation, and an estimation network based on auto-regressive layers. Note that when an abnormal sample is put into the network trained with only normal samples, the reconstruction error and ...
Anomaly detectionDilated convolutionalDeep autoencoderUnsupervised learningMemory augmentedDuring the operation of rotating machinery, the occurrence of unknown fault... W Li,Z Shang,GS Qian - Engineering Applications of Artificial Intelligence: The International Journal of Intelligent Real-Time Automation 被...
To improve the generator's training, generative adversarial network has been deployed to tackle the problem of the autoencoder which is susceptible to noise. The generator uses an autoencoder and decoder structure. In addition, the memory augments module is introduced to the autoencoder's sub-...