  B-decay data from the Belle experiment at the KEKB collider have a
substantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress this
we employ deep neural network algorithms. These provide improved signal from
background discrimination. However, the deep neural network develops a
substantial correlation with the $\Delta E$ kinematic variable used to
distinguish signal from background in the final fit due to its relationship
with input variables. The effect of this correlation is reduced by deploying an
adversarial neural network. Overall the adversarial deep neural network
performs better than a Boosted Decision Tree algorithimn and a commercial
package, NeuroBayes, which employs a neural net with a single hidden layer.
