Very Deep Convolutional Networks for Large-Scale Image Recognition
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...5) Networks and Training Settings: The VGG network [49] in this work was pretrained on ImageNet [60]....
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...Our main contributions are: • We propose a U-Net architecture [47], [48] with skip connections for the generator network; • A refinement learning approach is designed to stabilise the training of GAN for fast convergence and less parameter tuning; • The adversarial loss is coupled with a novel content loss considering both pixel-wise mean square error (MSE) and perceptual loss defined by pretrained deep convolutional networks from the Visual Geometry Group at Oxford University (in short VGG networks [49]) to achieve better reconstruction details; • Frequency domain information of the CS-MRI has been incorporated as additional constraints for the data consistency, which is formed as an extra loss term; • We perform comprehensive experiments and compare our proposed models with both classic CS-MRI and newly developed deep learning based methods....
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