  Generative adversarial networks (GANs) have shown tremendous promise in
learning to generate data and effective at aiding semi-supervised
classification. However, to this point, semi-supervised GAN methods make the
assumption that the unlabeled data set contains only samples of the joint
distribution of the classes of interest, referred to as inliers. Consequently,
when presented with a sample from other distributions, referred to as outliers,
GANs perform poorly at determining that it is not qualified to make a decision
on the sample. The problem of discriminating outliers from inliers while
maintaining classification accuracy is referred to here as the DOIC problem. In
this work, we describe an architecture that combines self-organizing maps
(SOMs) with SS-GANS with the goal of mitigating the DOIC problem and
experimental results indicating that the architecture achieves the goal.
Multiple experiments were conducted on hyperspectral image data sets. The
SS-GANS performed slightly better than supervised GANS on classification
problems with and without the SOM. Incorporating the SOMs into the SS-GANs and
the supervised GANS led to substantially mitigation of the DOIC problem when
compared to SS-GANS and GANs without the SOMs. Furthermore, the SS-GANS
performed much better than GANS on the DOIC problem, even without the SOMs.
