are obtained by testing the network on each of the three background sets separately, and performing a classification against the central signal sample for each one. Also shown are the area-under-curve (AUC) scores for each curve as well as the score for all the background test samples combined. The network performance is strongly dependent on the direction that the sample has been smeared in. This can be understood from the observables in Fig. \[fig:observables\] where there is a larger overlap between the signal distribution and the background which has been smeared upwards.

We now extend this classifier with an adversarial network which is designed to discriminate the smearing class that the background sample came from, based upon the output of the classifier. The aim for such an extension to the classifier is to attempt to remove such a large dependence of its performance on the smearing of the background [@Louppe:2016ylz; @Englert:2018cfo]. The adversary and classifier are forced to take part in a zero-sum game—the classifier must learn to make its prediction without using any information derived from the smearing, in order to make it as hard as possible for the adversary to be able to discriminate the background samples. This 