  Recurrent neural networks with differentiable attention mechanisms have had
success in generative and classification tasks. We show that the classification
performance of such models can be enhanced by guiding a randomly initialized
model to attend to salient regions of the input in early training iterations.
We further show that, if explicit heuristics for guidance are unavailable, a
model that is pretrained on an unsupervised reconstruction task can discover
good attention policies without supervision. We demonstrate that increased
efficiency of the attention mechanism itself contributes to these performance
improvements. Based on these insights, we introduce bootstrapped glimpse
mimicking, a simple, theoretically task-general method of more effectively
training attention models. Our work draws inspiration from and parallels
results on human learning of attention.
