Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection

In present study, we proposed a general framework based on a convolutional kernel and a variational autoencoder (CVAE) for anomaly detection on both complex image and vector datasets. The main idea is to maximize mutual information (MMI) through regularizing key information as follows: (1) the features between original input and the representation of latent space, (2) that between the first convolutional layer output and the last convolutional layer input, (3) original input and output of the decoder to train the model. Therefore, the proposed CVAE is optimized by combining the representations learned across the three different objectives targeted at MMI on both local and global variables with the original training objective function of Kullback–Leibler divergence distributions. It allowed achieving the additional supervision power for the detection of image and vector data anomalies using convolutional and fully connected layers, respectively. Our proposal CVAE combined by regularizing multiple discriminator spaces to detect anomalies was introduced for the first time as far as we know. To evaluate the reliability of the proposed CVAE-MMI, it was compared with the convolutional autoencoder-based model using the original objective function. Furthermore, the performance of our network was compared over state-of-the-art approaches in distinguishing anomalies concerning both image and vector datasets. The proposed structure outperformed the state-of-the-arts with high and stable area under the curve values.