Visualizing and Understanding Convolutional Networks
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved.
Also cited · not yet reviewed (1)
- DeCAF2013 · cited 2דThe generalization ability of convnet features is also explored in concurrent work by (Donahue et al. 2013).”From this paper · §Introduction
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- VGG2014 · cited 6×, 1 in Method“In our experiments, we evaluated models trained at two fixed scales: S=256S=256 (which has been widely used in the prior art (Krizhevsky et al. 2012; Zeiler & Fergus 2013; Sermanet et al. 2014)) and S=384S=384.”From VGG · §Classification Framework
- GoogLeNet (Inception)2014 · cited 2דVariants of this basic design are prevalent in the image classification literature and have yielded the best results to-date on MNIST, CIFAR and most notably on the ImageNet classification challenge [9, 21].”From GoogLeNet (Inception) · §Related Work
Abstract
Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. 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.