The emergence of visual semantics through communication games
This work addresses a bottleneck in emergent communication research by enabling more unbiased learning of visual semantics, though it is incremental in bridging self-supervised learning with communication games.
The study tackled the problem of how pretrained feature extractors bias emergent communication in referential signaling games with images, showing that self-supervised methods can learn visual semantics without supervised pretraining.
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.