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. Matthew D Zeiler, Rob Fergus, "Vis…

We also perform an ablation study to discover the performance contribution from different model layers. Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Networks" https://arxiv.org/abs/1311.2901 CNNの可視化を行い…

Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al. on the ImageNet classification benchmark. Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Net…

We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Networks" http…

In this paper we address both issues. Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Networks" https://arxiv.org/abs/1311.2901 CNNの可視化を行い、AlexNetの問題点を明らかにして改良することで、2013年のILSVRCの優勝…

However there is no clear understanding of why they perform so well, or how they might be improved. Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Networks" https://arxiv.org/abs/1311.2901 CNNの可視化を行い、Ale…

Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark (Krizhevsky et al., 2012). Matthew D Zeiler, Rob Fergus, "Visualizing and Understanding Convolutional Networks" h…