the classifier is severely affected. We find a value of 100 works well for our setup. There is another approach to training the adversarial network, where one updates the weights of both networks simultaneously. However, we find the approach of alternating the training—where the classifier is trained with the adversary weights frozen, and vice versa—to be more stable.

In Fig. \[fig:advcls\], we show the performance of the adversarial classifier through the classifier output and ROC curves. The adversary has clearly had the effect of shaping the classifier outputs such that their dependence on the background smearing has been almost entirely removed. Thus, the ROC curves and AUC scores become very close together since the classification performance is now barely affected by the smearing.

Extension to unsupervised autoencoder {#sec:unsupervised}
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Adversarial autoencoder
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[0.49]{} ![Autoencoder loss (a) and ROC curves (b) for an autoencoder trained only on background events. The three background distributions result from the three different directions of smearing.[]{data-label="fig:AE"}](figures/AE3response.pdf "fig:"){width="\textwidth"}

[0.49]{} ![Autoencoder loss (a) and ROC curves (b) for an autoencoder trained only on background events. The three background distributions result from the three different directions of smearing.[]{data-label="fig:AE"}](figures/AE3ROC.pdf "fig:"){width="\textwidth"}

As described earlier, autoencoders are an unsupervised learning algorithm which 