It has proven beneficial for both "deep" (Krizhevsky et al., 2012;Zeiler and Fergus, 2014) and "shallow" (Chatfield et al., 2014) systems, and it was recently introduced to NLP tasks (Zhang and LeCun, 2015).
in order to train the mapping function using both annotated data and points that are representative of the full search space, we rely on a form of data augmentation that we call visual chimera creation.