  The emergence of communication systems between agents which learn to play
referential signalling games with realistic images has attracted a lot of
attention recently. The majority of work has focused on using fixed, pretrained
image feature extraction networks which potentially bias the information the
agents learn to communicate. In this work, we consider a signalling game
setting in which a `sender' agent must communicate the information about an
image to a `receiver' who must select the correct image from many distractors.
We investigate the effect of the feature extractor's weights and of the task
being solved on the visual semantics learned by the models. We first
demonstrate to what extent the use of pretrained feature extraction networks
inductively bias the visual semantics conveyed by emergent communication
channel and quantify the visual semantics that are induced.
  We then go on to explore ways in which inductive biases can be introduced to
encourage the emergence of semantically meaningful communication without the
need for any form of supervised pretraining of the visual feature extractor. We
impose various augmentations 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 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. Our work
bridges a gap between emergent communication research and self-supervised
feature learning.
