  This paper studies the problem of measuring and predicting how memorable an
image is to pattern recognition machines, as a path to explore machine
intelligence. Firstly, we propose a self-supervised machine memory
quantification pipeline, dubbed ``MachineMem measurer'', to collect machine
memorability scores of images. Similar to humans, machines also tend to
memorize certain kinds of images, whereas the types of images that machines and
humans memorize are different. Through in-depth analysis and comprehensive
visualizations, we gradually unveil that``complex" images are usually more
memorable to machines. We further conduct extensive experiments across 11
different machines (from linear classifiers to modern ViTs) and 9 pre-training
methods to analyze and understand machine memory. This work proposes the
concept of machine memorability and opens a new research direction at the
interface between machine memory and visual data.
