Large Convolutional Network models have recently demonstrated
impressive classification performance on the ImageNet benchmark
Krizhevsky et al. [18]. However there is no clear understanding of
why they perform so well, or how they might be improved. In this paper
we explore 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-ofthe-
art results on Caltech-101 and Caltech-256 datasets.
