Very Deep Convolutional Networks for Large-Scale Image Recognition
Citations
324 citations
Cites background or methods from "Very Deep Convolutional Networks fo..."
..., we employ the 16 layer DeepNet model [31]....
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...It was shown independently by many authors [31, 4], that successively increasing the number of parameters during training typically yields better performance due to better initialization of larger models....
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...They have been shown to achieve state-of-the-art performance in a variety of vision problems, including image classification [19, 31], object detection [11], human pose estimation [32], stereo [36], and caption generation [15, 24, 35, 8, 14, 10]....
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...Whereas the latter combines dense conditional random fields [17] with the fully convolutional networks presented by Long et al. [21], we employ and modify the 16 layer DeepNet architecture presented in work by Simonyan and Zisserman [31]....
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...[21], we employ and modify the 16 layer DeepNet architecture presented in work by Simonyan and Zisserman [31]....
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323 citations
Cites background from "Very Deep Convolutional Networks fo..."
...Previous studies (Simonyan and Zisserman 2014; Tran et al. 2015) have demonstrated that smaller convolutional kernels are more efficient in ConvNet design....
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323 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...We adopt the VGG-16 architecture [27] and modify it for the purpose of our task....
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323 citations
Cites background from "Very Deep Convolutional Networks fo..."
..., pre-trained VGG-16 [36]) feature domain....
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...In all of our experiments, we compute content loss at layer relu4 2 and style loss at layer relu1 2, relu2 2, relu3 2, and relu4 2 of the pre-trained VGG-16 network....
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...DeepDream [1] may be the first attempt to generate artistic work using CNN. Inspired by this work, Gatys et al. [12] successfully applies CNN (pre-trained VGG-16 networks) to neural style transfer and produces more impressive stylization results compared to classic texture transfer methods....
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...(4) where F l and Gi are respectively feature map and Gram matrix computed from layer l of VGG-16 network [36](pretrained on the ImageNet dataset [34])....
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...These CNN algorithms either apply an iterative optimization mechanism [12], or directly learn a feed-forward generator network [19, 37] to seek an image close to both the content image and the style image – all measured in the CNN (i.e., pre-trained VGG-16 [36]) feature domain....
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322 citations
Cites background from "Very Deep Convolutional Networks fo..."
...The first is the VGG architecture of [22]....
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References
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