# What is the difference between out of distribution detection and anomaly detection?

I'm currently reading the paper Likelihood Ratios for Out-of-Distribution Detection, and it seems that their problem is very similar to the problem of anomaly detection. More precisely, given a neural network trained on a dataset consisting of classes $$A,B,$$ and $$C$$, then they can detect if an input to the neural network is anomalous if it is different than these three classes. What is the difference between what they are doing and regular anomaly detection?