  In recent years, the widespread use of deep neural networks (DNNs) has
facilitated great improvements in performance for computer vision tasks like
image classification and object recognition. In most realistic computer vision
applications, an input image undergoes some form of image distortion such as
blur and additive noise during image acquisition or transmission. Deep networks
trained on pristine images perform poorly when tested on such distortions. In
this paper, we evaluate the effect of image distortions like Gaussian blur and
additive noise on the activations of pre-trained convolutional filters. We
propose a metric to identify the most noise susceptible convolutional filters
and rank them in order of the highest gain in classification accuracy upon
correction. In our proposed approach called DeepCorrect, we apply small stacks
of convolutional layers with residual connections, at the output of these
ranked filters and train them to correct the worst distortion affected filter
activations, whilst leaving the rest of the pre-trained filter outputs in the
network unchanged. Performance results show that applying DeepCorrect models
for common vision tasks like image classification (ImageNet), object
recognition (Caltech-101, Caltech-256) and scene classification (SUN-397),
significantly improves the robustness of DNNs against distorted images and
outperforms other alternative approaches..
