Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Compositional Obverter Communication Learning from Raw Visual Input
Edward Choi, Angeliki Lazaridou, Nando de Freitas
Feb 15, 2018 (modified: Feb 28, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features). Humans, however, do not learn to communicate based on well-summarized features. In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols. The agents play an image description game where the image contains factors such as colors and shapes. We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding. Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.
TL;DR:We train neural network agents to develop a language with compositional properties from raw pixel input.
Keywords:compositional language, obverter, multi-agent communication, raw pixel input
Enter your feedback below and we'll get back to you as soon as possible.