Limitations Our evaluation setup in the discriminative training case is not ideal: the negative samples in both the train and test sets are artificially created by the same mechanism.
high performance on the test set in the discriminative case may result from "leakage" in which the model learns to rely on idiosyncrasies and artifcats of the data generation mechanism.