Very Deep Convolutional Networks for Large-Scale Image Recognition
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...Table 8 compares full MobileNet to the original GoogleNet [30] and VGG16 [27]....
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...The general trend has been to make deeper and more complicated networks in order to achieve higher accuracy [27, 31, 29, 8]....
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...MobileNet is nearly as accurate as VGG16 while being 32 times smaller and 27 times less compute intensive....
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...We remove the fully connected layers of VGG16 which makes the SegNet encoder network significantly smaller and easier to train than many other recent architectures [2], [4], [11], [18]....
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...The encoder network in SegNet is topologically identical to the convolutional layers in VGG16 [1]....
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...In this work we discard the fully connected layers of the VGG16 encoder network which enables us to train the network using the relevant training set using SGD optimization....
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...Most recent deep architectures for segmentation have identical encoder networks, i.e VGG16, but differ in the form of the decoder network, training and inference....
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...The encoder network which produces these low resolution representations in all of these architectures is the VGG16 classification network [1] which has 13 convolutional layers and three fully...
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References
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