  Memorability is considered to be an important characteristic of visual
content, whereas for advertisement and educational purposes it is often
crucial. Despite numerous studies on understanding and predicting image
memorability, there are almost no achievements in memorability modification. In
this work, we study two approaches to image editing - GAN and classical image
processing - and show their impact on memorability. The visual features which
influence memorability directly stay unknown till now, hence it is impossible
to control it manually. As a solution, we let GAN learn it deeply using labeled
data, and then use it for conditional generation of new images. By analogy with
algorithms which edit facial attributes, we consider memorability as yet
another attribute and operate with it in the same way. Obtained data is also
interesting for analysis, simply because there are no real-world examples of
successful change of image memorability while preserving its other attributes.
We believe this may give many new answers to the question "what makes an image
memorable?" Apart from that we also study the influence of conventional
photo-editing tools (Photoshop, Instagram, etc.) used daily by a wide audience
on memorability. In this case, we start from real practical methods and study
it using statistics and recent advances in memorability prediction.
Photographers, designers, and advertisers will benefit from the results of this
study directly.
