  Image quality plays a big role in CNN-based image classification performance.
Fine-tuning the network with distorted samples may be too costly for large
networks. To solve this issue, we propose a transfer learning approach
optimized to keep into account that in each layer of a CNN some filters are
more susceptible to image distortion than others. Our method identifies the
most susceptible filters and applies retraining only to the filters that show
the highest activation maps distance between clean and distorted images.
Filters are ranked using the Borda count election method and then only the most
affected filters are fine-tuned. This significantly reduces the number of
parameters to retrain. We evaluate this approach on the CIFAR-10 and CIFAR-100
datasets, testing it on two different models and two different types of
distortion. Results show that the proposed transfer learning technique recovers
most of the lost performance due to input data distortion, at a considerably
faster pace with respect to existing methods, thanks to the reduced number of
parameters to fine-tune. When few noisy samples are provided for training, our
filter-level fine tuning performs particularly well, also outperforming state
of the art layer-level transfer learning approaches.
