  Neural networks follow a gradient-based learning scheme, adapting their
mapping parameters by back-propagating the output loss. Samples unlike the ones
seen during training cause a different gradient distribution. Based on this
intuition, we design a novel semi-supervised anomaly detection method called
R2-AD2. By analysing the temporal distribution of the gradient over multiple
training steps, we reliably detect point anomalies in strict semi-supervised
settings. Instead of domain dependent features, we input the raw gradient
caused by the sample under test to an end-to-end recurrent neural network
architecture. R2-AD2 works in a purely data-driven way, thus is readily
applicable in a variety of important use cases of anomaly detection.
