  There has been an increasing interest in the area of emergent communication
between agents which learn to play referential signalling games with realistic
images. In this work, we consider the signalling game setting of Havrylov and
Titov and investigate the effect of the feature extractor's weights and of the
task being solved on the visual semantics learned or captured by the models. We
impose various augmentation to the input images and additional tasks in the
game with the aim to induce visual representations which capture conceptual
properties of images. Through our set of experiments, we demonstrate that
communication systems which capture visual semantics can be learned in a
completely self-supervised manner by playing the right types of game.
