Very Deep Convolutional Networks for Large-Scale Image Recognition
Citations
1,954 citations
1,953 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...We achieved the best results using RMSprop [45], which is an adaptive step size method that scales the gradient of each weight by a running average of its gradient magnitudes....
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1,947 citations
Cites methods from "Very Deep Convolutional Networks fo..."
...To make the detector faster, we take the reduced VGG [32] base network architecture from SSD [22], sample half of the channels per layer and change all max pooling layers to convolution layers with 3×3 kernel size and stride of 2....
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...In Faster R-CNN, we use the VGG16 [32] and 3 anchor scales (16, 32, 48) and 3 aspect ratios ( 12 , 1, 2)....
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...The baseline methods trained VGG [26] models on ground truth boxes of RGB-D images and adopt the same box parameter and loss functions as our main method....
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...The baseline methods trained VGG [32] models on ground truth boxes of RGB-D images and adopt the same box parameter and loss functions as our main method....
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1,902 citations
1,894 citations
Cites background or methods from "Very Deep Convolutional Networks fo..."
...For instance, two 3 3 cascaded convolutional layers have the same effective receptive field of one 5 5 layer, but fewer weights [36]....
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...At the same time, it has the advantages of applying more non-linearities and being less prone to overfitting because small kernels have fewer weights than bigger kernels [36]....
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...In this paper, inspired by the groundbreaking work of Simonyan and Zisserman [36] on deep CNNs, we investigate the potential of using deep architectures with small convolutional kernels for segmentation of gliomas in MRI images....
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...Simonyan and Zisserman proposed the use of small 3 3 kernels to obtain deeper CNNs....
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References
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