  Attention is an important component of modern deep learning. However, less
emphasis has been put on its inverse: ignoring distraction. Our daily lives
require us to explicitly avoid giving attention to salient visual features that
confound the task we are trying to accomplish. This visual prioritisation
allows us to concentrate on important tasks while ignoring visual distractors.
  In this work, we introduce Neural Blindness, which gives an agent the ability
to completely ignore objects or classes that are deemed distractors. More
explicitly, we aim to render a neural network completely incapable of
representing specific chosen classes in its latent space. In a very real sense,
this makes the network "blind" to certain classes, allowing and agent to focus
on what is important for a given task, and demonstrates how this can be used to
improve localisation.
