  Generative adversarial networks (GANs) are well known for their unsupervised
learning capabilities. A recent success in the field of astronomy is deblending
two overlapping galaxy images via a branched GAN model. However, it remains a
significant challenge to comprehend how the network works, which is
particularly difficult for non-expert users. This research focuses on behaviors
of one of the network's major components, the Discriminator, which plays a
vital role but is often overlooked, Specifically, we enhance the Layer-wise
Relevance Propagation (LRP) scheme to generate a heatmap-based visualization.
We call this technique Polarized-LRP and it consists of two parts i.e. positive
contribution heatmaps for ground truth images and negative contribution
heatmaps for generated images. Using the Galaxy Zoo dataset we demonstrate that
our method clearly reveals attention areas of the Discriminator when
differentiating generated galaxy images from ground truth images. To connect
the Discriminator's impact on the Generator, we visualize the gradual changes
of the Generator across the training process. An interesting result we have
achieved there is the detection of a problematic data augmentation procedure
that would else have remained hidden. We find that our proposed method serves
as a useful visual analytical tool for a deeper understanding of GAN models.
