Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark . However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.
Zeiler and Fergus REF presented a novel visualization method that gives insight into the function of intermediate feature layers and the operation of the classifier, and showed how these visualization can be used into identify problems of the model and so obtain better performance.
Visualizing and Understanding Convolutional Networks
ArXiv
ArXiv
Computer Science