Anomalies, often referred to as outliers, are data points or patterns in data that do not conform to a notion of normal behavior. Anomaly detection, then, is the task of finding those patterns in data that do not adhere to expected norms. The capability to recognize or detect anomalous behavior can provide highly useful insights across industries.
Flagging or enacting a planned response when these unusual cases occur can save businesses time, money, and customers. Automatically detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over the years. Traditional machine learning approaches are sub-optimal when it comes to high dimensional data, because they fail to capture the complex structure in the data.
This is where deep learning methods can be leveraged for the task.