  This research study proposes using Generative Adversarial Networks (GAN) that
incorporate a two-dimensional measure of human memorability to generate
memorable or non-memorable images of scenes. The memorability of the generated
images is evaluated by modelling Visual Memory Schemas (VMS), which correspond
to mental representations that human observers use to encode an image into
memory. The VMS model is based upon the results of memory experiments conducted
on human observers, and provides a 2D map of memorability. We impose a
memorability constraint upon the latent space of a GAN by employing a VMS map
prediction model as an auxiliary loss. We assess the difference in memorability
between images generated to be memorable or non-memorable through an
independent computational measure of memorability, and additionally assess the
effect of memorability on the realness of the generated images.
