  Learning representations that clearly distinguish between normal and abnormal
data is key to the success of anomaly detection. Most of existing anomaly
detection algorithms use activation representations from forward propagation
while not exploiting gradients from backpropagation to characterize data.
Gradients capture model updates required to represent data. Anomalies require
more drastic model updates to fully represent them compared to normal data.
Hence, we propose the utilization of backpropagated gradients as
representations to characterize model behavior on anomalies and, consequently,
detect such anomalies. We show that the proposed method using gradient-based
representations achieves state-of-the-art anomaly detection performance in
benchmark image recognition datasets. Also, we highlight the computational
efficiency and the simplicity of the proposed method in comparison with other
state-of-the-art methods relying on adversarial networks or autoregressive
models, which require at least 27 times more model parameters than the proposed
method.
