Comparing a composite model versus chained models to locate a nearest
  visual object
Antoine Le Borgne Xavier Marjou Fanny Parzysz Tayeb Lemlouma
Extracting information from geographic images and text is crucial for
autonomous vehicles to determine in advance the best cell stations to connect
to along their future path. Multiple artificial neural network models can
address this challenge; however, there is no definitive guidance on the
selection of an appropriate model for such use cases. Therefore, we
experimented two architectures to solve such a task: a first architecture with
chained models where each model in the chain addresses a sub-task of the task;
and a second architecture with a single model that addresses the whole task.
Our results showed that these two architectures achieved the same level
performance with a root mean square error (RMSE) of 0.055 and 0.056; The
findings further revealed that when the task can be decomposed into sub-tasks,
the chain architecture exhibits a twelve-fold increase in training speed
compared to the composite model. Nevertheless, the composite model
significantly alleviates the burden of data labeling.