  When training deep neural networks, it is typically assumed that the training
examples are uniformly difficult to learn. Or, to restate, it is assumed that
the training error will be uniformly distributed across the training examples.
Based on these assumptions, each training example is used an equal number of
times. However, this assumption may not be valid in many cases. "Oddball SGD"
(novelty-driven stochastic gradient descent) was recently introduced to drive
training probabilistically according to the error distribution - training
frequency is proportional to training error magnitude. In this article, using a
deep neural network to encode a video, we show that oddball SGD can be used to
enforce uniform error across the training set.
