Interpreting Galaxy Deblender GAN from the Discriminator’s Perspective

In large galaxy surveys it can be difficult to separate overlapping galaxies, a process called deblending. Generative adversarial networks (GANs) have shown great potential in addressing this fundamental problem. However, it remains a significant challenge to comprehend how the network works, which is particularly difficult for non-expert users. This research focuses on understanding the behaviors of one of the network’s major components, the Discriminator, which plays a vital role but is often overlooked. Specifically, we propose an enhanced Layer-wise Relevance Propagation (LRP) algorithm called Polarized-LRP. It generates a heatmap-based visualization highlighting the area in the input image that contributes to the network decision. It consists of two parts i.e. a positive contribution heatmap for the images classified as ground truth and a negative contribution heatmap for the ones classified as generated. As a use case, we have chosen the deblending of two overlapping galaxy images via a branched GAN model. Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals the attention areas of the Discriminator to differentiate generated galaxy images from ground truth images, and outperforms the original LRP method. To connect the Discriminator’s impact on the Generator, we also visualize the attention shift of the Generator across the training process. An interesting result we have achieved 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 more effective training and a deeper understanding of GAN models.