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.

研究神经网络在图像分类中受质量失真的影响，提出了一种基于混合专家模型的解决方案，通过权重共享方式减少网络参数数量并提高性能。

高质量韧性深度神经网络

Quality Resilient Deep Neural Networks

Image quality is an important practical challenge that is often overlooked in
the design of machine vision systems. Commonly, machine vision systems are
trained and tested on high quality image datasets, yet in practical
applications the input images can not be assumed to be of high quality.
Recently, deep neural networks have obtained state-of-the-art performance on
many machine vision tasks. In this paper we provide an evaluation of 4
state-of-the-art deep neural network models for image classification under
quality distortions. We consider five types of quality distortions: blur,
noise, contrast, JPEG, and JPEG2000 compression. We show that the existing
networks are susceptible to these quality distortions, particularly to blur and
noise. These results enable future work in developing deep neural networks that
are more invariant to quality distortions.

本文评估了四种最先进的深度神经网络模型在图像分类方面对模糊、噪声、对比度、JPEG 和 JPEG2000 压缩等五种图像质量扭曲的表现，结果表明现有网络对图像质量扭曲特别是模糊和噪声较为敏感，这为未来开发更具抗图像质量扭曲能力的深度神经网络开展了研究。