Very Deep Convolutional Networks for Large-Scale Image Recognition
Citations
450 citations
449 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...Class saliency maps (Simonyan and Zisserman, 2014) were used (Poplin et al....
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...Class saliency maps (Simonyan and Zisserman, 2014) were used (Poplin et al., 2018) to highlight parts of the fundus image which were the most discriminative for the CNN when predicting individual sex, age and blood pressure (Fig....
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449 citations
449 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...We apply the trained model with VGG-16 and adopt the same inference procedure as our method....
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...For fair comparisons, we also implemented our method with a ResNet-18 encoder, which has less parameters compared to the VGG-16 in [74, 8] and the 3D convolutional ResNet-18 in [69]....
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...We use the open-sourced pre-trained VGG-16 [58] model and adopt our proposed inference procedure....
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449 citations
Cites background or methods from "Very Deep Convolutional Networks fo..."
...more state-of-the-art architectures are used as baseline models in many works, including network in networks (NIN) [68], VGG nets [69] and residual networks (ResNet) [70]....
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...Baseline Models Representative Works Alexnet [1] structural matrix [30]–[32] low-rank factorization [39] Network in network [68] low-rank factorization [39] VGG nets [69] transfered filters [43] low-rank factorization [39] Residual networks [70] compact filters [48], stochastic depth [61] parameter sharing [25] All-CNN-nets [67] transfered filters [44] LeNets [66] parameter sharing [25] parameter pruning [21], [23]...
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References
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