Very Deep Convolutional Networks for Large-Scale Image Recognition
Citations
1,521 citations
Cites background from "Very Deep Convolutional Networks fo..."
...Deep neural networks are the basis of state-of-the-art results for image recognition [17, 23, 25], object detection [7], face recognition [26], speech recognition [8], machine translation [24], image caption generation [28], and driverless car technology [14]....
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1,511 citations
1,508 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...With a deeper network architecture (VGG-VD, a network with 16 weight layers from [34]), we achieve 87....
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1,487 citations
1,485 citations
Cites background or methods from "Very Deep Convolutional Networks fo..."
...Whereas AlexNet had 5 convolutional layers [1], the VGG network and GoogLeNet in 2014 had 19 and 22 layers respectively [5, 7], and most recently the ResNet architecture featured 152 layers [8]....
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...Since then there has been a notable shift towards CNNs in many areas of computer vision [3, 4, 5, 6, 7, 8]....
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...Network depth is a major determinant of model expressiveness, both in theory [9, 10] and in practice [5, 7, 8]....
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References
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