Very Deep Convolutional Networks for Large-Scale Image Recognition
Citations
854 citations
854 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...In formulas LC(x;y) = ∑ l∈LC Nl∑ i=1 ‖F li (x)− F li (y)‖22 , (6) where Nl is the number of maps (feature channels) in layer l of the descriptor CNN....
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...In practice, (Gatys et al., 2015a;b) use as texture descriptor the combination of several Gram matrices Gl, l ∈ LT , where LT contains selected indices of convolutional layer in the descriptor CNN....
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...Our loss function is derived from (Gatys et al., 2015a;b) and compares image statistics extracted from a fixed pretrained descriptor CNN (usually one of the VGG CNN (Simonyan & Zisserman, 2014; Chatfield et al., 2014) which are pre-trained for image classification on the ImageNet ILSVRC 2012 data)....
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...The descriptor CNN is used to measure the mismatch between the prototype texture x0 and the generated image x. Denote by F li (x) the i-th map (feature channel) computed by the l-th convolutional layer by the descriptor CNN applied to image x....
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...Qualitatively, our generator CNN and (Gatys et al., 2015a)’s results are comparable and superior to the other methods; however, the generator CNN is much more efficient (see Sect....
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852 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...Inspired by [16, 35], we build a CNN with three preceding 3×3 convolutional layers followed by six Inception modules and two fully connected layers....
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851 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...Another widely used algorithm, even faster than YOLO, is the Single Shot Detector (SSD) [138], which uses standard DCNN architectures such as VGG [131] to achieve competitive results on public benchmarks....
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849 citations
Cites background from "Very Deep Convolutional Networks fo..."
...There are many well-known architectures for CNNs (i.e., VGG-16 (Simonyan and Zisserman, 2014), Microsoft ResNet-152 (He et al., 2016), and GoogleNet (Szegedy et al., 2015))....
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..., VGG-16 (Simonyan and Zisserman, 2014), Microsoft ResNet-152 (He et al....
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References
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