Create quality resilient deep neural networks by using a set of “expert” models. These experts are trained on different quality distortions and their output is weighed by a separate gating network which appropriately matches input distortions to the kinds that each expert is trained on.
Applying Ensemble Method to Augment Image Quality Resilience
Post · Mar 24, 2017 18:11 · Share on Twitter
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.