1. A method of facilitating anomaly detection via a multi-neural-network architecture, the method being implemented by a computer system that comprises one or more processors executing computer program instructions that, when executed, perform the method, the method comprising: obtaining data items that correspond to a concept; providing the data items to a first neural network to cause the first neural network to generate hidden representations of the data items from the data items; providing the hidden representations of the data items to a second neural network to cause the second neural network to generate reconstructions of the data items from the hidden representations of the data items; providing the reconstructions of the data items as reference feedback to the first neural network to cause the first neural network to assess the reconstructions of the data items against the data items, the first neural network updating one or more representation-generation-related configurations of the first neural network based on the first neural network's assessment of the reconstructions of the data items; and subsequent to providing the reconstructions of the data items, performing the following operations: providing a first data item to the first neural network to cause the first neural network to generate a hidden representation of the first data item from the first data item; providing the hidden representation of the first data item to the second neural network to cause the second neural network to generate a reconstruction of the first data item from the hidden representation of the first data item; and detecting an anomaly in the first data item based on differences between the first data item and the reconstruction of the first data item.