  We used Data Maps to model and characterize the AuTexTification dataset. This
provides insights about the behaviour of individual samples during training
across epochs (training dynamics). We characterized the samples across 3
dimensions: confidence, variability and correctness. This shows the presence of
3 regions: easy-to-learn, ambiguous and hard-to-learn examples. We used a
classic CNN architecture and found out that training the model only on a subset
of ambiguous examples improves the model's out-of-distribution generalization.
