Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Table 3: Communicative success of agents playing different games. Columns random, train and test report percentage accuracies. Column topographic ρ reports the topographic similarity between the symbolic representation of scenes and the generated messages (p<0.01, permutation test).
['[BOLD] game', '[BOLD] distractors', '[BOLD] balanced', '[BOLD] viewpoints', '[BOLD] lexicon size', '[BOLD] random', '[BOLD] train', '[BOLD] test', '[BOLD] topographic  [ITALIC] ρ']
Since we do not pre-train their ConvNets on an object recognition task, the dense representations u used to derive the message contain no bias towards any image- or scene-specific information (e.g, object color, shape or location). The extraction of visual properties is thus driven entirely by the communication game. Moreover, we also report the topographic similarity (column topographic ρ) between the symbolic attribute-based representations of scenes (floor color, object color, shape and location) and the generated messages.