  We study deep neural networks for classification of images with quality
distortions. We first show that networks fine-tuned on distorted data greatly
outperform the original networks when tested on distorted data. However,
fine-tuned networks perform poorly on quality distortions that they have not
been trained for. We propose a mixture of experts ensemble method that is
robust to different types of distortions. The "experts" in our model are
trained on a particular type of distortion. The output of the model is a
weighted sum of the expert models, where the weights are determined by a
separate gating network. The gating network is trained to predict optimal
weights for a particular distortion type and level. During testing, the network
is blind to the distortion level and type, yet can still assign appropriate
weights to the expert models. We additionally investigate weight sharing
methods for the mixture model and show that improved performance can be
achieved with a large reduction in the number of unique network parameters.
