  We present a novel variant of Domain Adversarial Networks with impactful
improvements to the loss functions, training paradigm, and hyperparameter
optimization. New loss functions are defined for both forks of the DANN
network, the label predictor and domain classifier, in order to facilitate more
rapid gradient descent, provide more seamless integration into modern neural
networking frameworks, and allow previously unavailable inferences into network
behavior. Using these loss functions, it is possible to extend the concept of
'domain' to include arbitrary user defined labels applicable to subsets of the
training data, the test data, or both. As such, the network can be operated in
either 'On the Fly' mode where features provided by the feature extractor
indicative of differences between 'domain' labels in the training data are
removed or in 'Test Collection Informed' mode where features indicative of
difference between 'domain' labels in the combined training and test data are
removed (without needing to know or provide test activity labels to the
network). This work also draws heavily from previous works on Robust Training
which draws training examples from a L_inf ball around the training data in
order to remove fragile features induced by random fluctuations in the data. On
these networks we explore the process of hyperparameter optimization for both
the domain adversarial and robust hyperparameters. Finally, this network is
applied to the construction of a binary classifier used to identify the
presence of EM signal emitted by a turbopump. For this example, the effect of
the robust and domain adversarial training is to remove features indicative of
the difference in background between instances of operation of the device -
providing highly discriminative features on which to construct the classifier.
