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

Uniform Learning in a Deep Neural Network via "Oddball" Stochastic Gradient Descent