Improved AutoEncoder with LSTM module and KL divergence

arXiv:2404.19247v1 Announce Type: new
Abstract: The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep convolutional autoencoder (CAE) network and deep supporting vector data description (SVDD) model have been universally employed and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE network for anomalous data can easily lead to high false negative rate in detecting anomalous data. On the other hand, the deep SVDD model has …

abstract anomaly anomaly detection arxiv autoencoder cae convolutional cs.cv cs.lg data dataset detection divergence however lstm network normal success type vector