  Overfitting is one of the most critical challenges in deep neural networks,
and there are various types of regularization methods to improve generalization
performance. Injecting noises to hidden units during training, e.g., dropout,
is known as a successful regularizer, but it is still not clear enough why such
training techniques work well in practice and how we can maximize their benefit
in the presence of two conflicting objectives---optimizing to true data
distribution and preventing overfitting by regularization. This paper addresses
the above issues by 1) interpreting that the conventional training methods with
regularization by noise injection optimize the lower bound of the true
objective and 2) proposing a technique to achieve a tighter lower bound using
multiple noise samples per training example in a stochastic gradient descent
iteration. We demonstrate the effectiveness of our idea in several computer
vision applications.
