As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this direction, we systematically explore the task of referential communication in a community of state-of-the-art pre-trained visual networks, showing that they can develop a shared protocol to refer to a target image among a set of candidates. Such shared protocol, induced in a self-supervised way, can to some extent be used to communicate about previously unseen object categories, as well as to make more granular distinctions compared to the categories taught to the original networks. Contradicting a common view in multi-agent emergent communication research, we find that imposing a discrete bottleneck on communication hampers the emergence of a general code. Moreover, we show that a new neural network can learn the shared protocol developed in a community with remarkable ease, and the process of integrating a new agent into a community more stably succeeds when the original community includes a larger set of heterogeneous networks. Finally, we illustrate the independent benefits of developing a shared communication layer by using it to directly transfer an object classifier from a network to another, and we qualitatively and quantitatively study its emergent properties.

翻译：随着大型预训练图像处理神经网络被嵌入自动驾驶汽车或机器人等自主智能体中，这些系统如何在架构和训练方式各异的条件下就周围世界进行通信的问题随之产生。作为该方向的第一步，我们系统性地探索了由当前最先进的预训练视觉网络构成的社区中的指代通信任务，证明它们可以发展出一套共享协议，用以在一组候选图像中指定目标图像。这种以自监督方式诱导出的共享协议，在一定程度上可用于就先前未见过的物体类别进行通信，并能在与原始网络所学类别相比更细粒度的区分中发挥作用。与多智能体涌现通信研究中的普遍观点相悖，我们发现对通信施加离散瓶颈会阻碍通用编码的涌现。此外，我们证明新神经网络能以显著简易性习得社区中发展的共享协议，且当原始社区包含更大规模的异构网络集合时，将新智能体整合到社区中的过程更为稳定。最后，我们通过直接利用该共享通信层将物体分类器从一个网络迁移至另一个网络，展示了独立发展共享通信层的优势，并定性与定量地研究了其涌现特性。