基于深度自编码器的弱监督异常检测算法研究

nomaly detection related technologies have been well explored in a great deal of application scenarios. However, in actual production and life, because there are often no or only a few abnormal samples in the data set, data over-fitting problems often occur, and it is more likely to be seriously affected by noise and large outliers, which promotes the research of weak supervised anomaly detection algorithm. Based on deep learning, this thesis proposes a new weak supervised anomaly detection algorithm model based on autoencoder. The coding part is based on four-layer long short-term memory network, the decoding part is based on the decoding block composed of three-layer one-dimensional convolution and deconvolution. A new joint training strategy is proposed to further solve the problem of data over-fitting. And comparative experiments are carried out from different dimensions, which reasonably verifies the progressiveness and robustness of the algorithm model designed in the thesis.