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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"Very Deep Convolutional Networks fo..." refers background in this paper
...Convolutional networks (ConvNets) have recently enjoyed a great success in large-scale visual recognition [10, 16, 17, 19] which has become possible due to the large public image repositories, such as ImageNet [4], and high-performance computing syste ms, such as GPUs or large-scale distributed clusters [3]....
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"Very Deep Convolutional Networks fo..." refers methods in this paper
...In particular, an important role in t he advance of deep visual recognition architectures has been played by the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [1], which has served as a testbed for a few generations of large-s cal image classification systems, from high-dimensional shallow feature encodings [13] (the winner of ILSVRC-2011) to deep ConvNets [10] (the winner of ILSVRC-2012)....
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