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:利用正常数据进行训练,学习到正常数据的”波动边界“,将超出边界的数据视为异常数据。无监督异常检测主要面临两大挑战:一是标签数据的获取需要人为的监督;二是高维...
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...
MNIST和CIFAR-10的内存大小N分别设置为100和500。 我们比较该模型与几个传统和基于深度学习的一般异常检测方法作为基线,包括看到下面成了SVM (OCSVM),核密度估计(KDE)[27],深变分autoencoder (VAE),深自回归模型生成PixCNN和深层结构能源模型(DSEBM) [42]。具体来说,对于密度估计方法(如KDE和PixCNN)和基于重构...
unofficial implementation of paper Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder (MemAE) for Unsupervised Anomaly Detection - lyn1874/memAE
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. [...
This paper proposes a regional geochemical anomaly identification method based on the memory-augmented autoencoder (MemAE), incorporating geological controlling factors. Firstly, the MemAE model is introduced to address the excessive generalization capability of the traditional autoencoder (AE) model. ...
Aiming at this problem, this paper proposed an unsupervised learning algorithm named Memory-augmented skip-connected deep autoencoder (Mem-SkipAE) for anomaly detection of rocket engines with multi-source data fusion. Unlike traditional autoencoders, the input embedding for the decoder is not ...
Zhao, X., Ren, Y., Du, Y., Zhang, S., Wang, N.: Improving item cold-start recommendation via model-agnostic conditional variational autoencoder. In: SIGIR, pp. 2595-2600 (2022) Jia, R., Zhou, X., Dong, L., Pan, S.: Hypergraph convolutional network for group recommendation. In...
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. [...
《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]...