Softmax函数
计算机科学
水准点(测量)
卷积神经网络
分类器(UML)
人工智能
机器学习
可视化
人工神经网络
模式识别(心理学)
大地测量学
地理
作者
Matthew D. Zeiler,Rob Fergus
出处
期刊:Cornell University - arXiv
日期:2013-11-12
被引量:270
摘要
Large Convolutional Neural Network models have recently demonstrated
impressive classification performance on the ImageNet benchmark \cite{Kriz12}.
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. We also
perform an ablation study to discover the performance contribution from
different model layers. This enables us to find model architectures that
outperform Krizhevsky \etal on the ImageNet classification benchmark. 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.
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