Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning
This work addresses robustness in image classification for CNNs, offering a more efficient fine-tuning method, though it is incremental as it builds on existing transfer learning techniques.
The paper tackles the problem of CNN performance degradation due to image distortion by proposing a selective filter-level fine-tuning method that identifies and retrains only the most susceptible filters, reducing parameters and training time. Results on CIFAR-10 and CIFAR-100 show it recovers most lost performance and outperforms layer-level approaches with few noisy samples.
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.