文件名称：2013-Visualizing and Understanding Convolutional Networks
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更新时间：2018-06-10 04:00:40
DeepLearning
Large Convolutional Network models have recently demonstrated impressive classication performance on the ImageNet benchmark (Krizhevsky et al., 2012). 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 classier. Used in a diagnostic role, these visualizations allow us to nd model architectures that outperform Krizhevsky et al. on the ImageNet classication benchmark. We also perform an ablation study to discover the performance contribution from dierent model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.