Knowledge Extraction from Auto-Encoders on Anomaly Detection Tasks Using Co-activation Graphs

Deep neural networks have exploded in popularity and different types of networks are used to solve a multitude of complex tasks. One such task is anomaly detection, that a type of deep neural network called auto-encoder has become extremely proficient at solving. The low level neural activity, produced by such a network, generates extremely rich representations of the data, which can be used to extract task specific knowledge. In this paper, we built upon previous work and used co-activation graph analysis to extract knowledge from auto-encoders, that were trained for the specific task of anomaly detection. First, we outlined a method for extracting co-activation graphs from auto-encoders. Then, we performed graph analysis to discover that task specific knowledge from the auto-encoder was being encoded into the co-activation graph, and that the extracted knowledge could be used to reveal the role of individual neurons in the network.