Visualizing and Understanding Convolutional Neural Networks
Year: 2013
Type: preprint
Abstract: Large Convolutional Neural Network models have recently demonstratedimpressive 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.
Source: arXiv (Cornell University)
Authors: Matthew D. Zeiler, Rob Fergus
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2206858481