  Datasets often contain input dimensions that are unnecessary to predict the
output label, e.g. background in object recognition, which lead to more
trainable parameters. Deep Neural Networks (DNNs) are robust to increasing the
number of parameters in the hidden layers, but it is unclear whether this holds
true for the input layer. In this letter, we investigate the impact of
unnecessary input dimensions on a central issue of DNNs: their data efficiency,
ie. the amount of examples needed to achieve certain generalization
performance. Our results show that unnecessary input dimensions that are
task-unrelated substantially degrade data efficiency. This highlights the need
for mechanisms that remove {task-unrelated} dimensions to enable data
efficiency gains.
