  Visual content often contains recurring elements. Text is made up of glyphs
from the same font, animations, such as cartoons or video games, are composed
of sprites moving around the screen, and natural videos frequently have
repeated views of objects. In this paper, we propose a deep learning approach
for obtaining a graphically disentangled representation of recurring elements
in a completely self-supervised manner. By jointly learning a dictionary of
texture patches and training a network that places them onto a canvas, we
effectively deconstruct sprite-based content into a sparse, consistent, and
interpretable representation that can be easily used in downstream tasks. Our
framework offers a promising approach for discovering recurring patterns in
image collections without supervision.
