Quality Robust Mixtures of Deep Neural Networks

We study deep neural networks for classification of images with quality distortions. Deep network performance on poor quality images can be greatly improved if the network is fine-tuned with distorted data. However, it is difficult for a single fine-tuned network to perform well across multiple distortion types. We propose a mixture of experts-based ensemble method, MixQualNet, that is robust to multiple different types of distortions. The...