Compositional Obverter Communication Learning From Raw Visual Input

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)... (read more)

Results in Papers With Code
(↓ scroll down to see all results)