string resnet The architecture of the backbone feature extractor to be used for training resnet, vgg, vanilla_unet, efficientnet_b0, vanilla_dynamic num_layers int 18 The depth of the feature extractor for scalable templates resnets: 10, 18, 34, 50, 101 vgg: 16, 19 ...
I also did not reproduce all the model configurations from the paper; the details of the differences will be explained in the section on the architecture. Here are some images generated with our CUDA implementation. The model is trained on elephant images from ImageNet 64x64 without class-...
The neural network architecture is derived from theU-netarchitecture (see thepaper). The loss function is the cross-entropy and the stochastic gradient descent is employed for optimization. The activation function after each convolutional layer is the Rectifier Linear Unit (ReLU), and a dropout of...
The neural network structure is derived from the U-Net architecture, described in this paper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Als...
We also apply the proposed architecture on the Attention UNet (AUNet) and the Residual UNet (ResUNet). We evaluated the proposed architectures on two publically available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and ...
In each residual block, we used three parallel convolutions in addition to the standard two-convolution set of the residual network architecture; that is, there were at most four parallel branches of stacked two-convolution sets. After convolutions, the output is added to the initial input with...
The decoder is trained with multiple resolutions of the labels, as explained in the next section on the training of HUT. In addition to this architecture, we proposed introducing extra upsampling and pooling layers to the first skip connection, as illustrated in Fig. 1. It increases the ...
The neural network structure is derived from theU-Netarchitecture, described in thispaper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Also on...
the implementation from the paperDiffusion Models Beat GANs on Image Synthesis. Currently the UNet only supports unconditioned diffusion training. I also did not reproduce all the model configurations from the paper; the details of the differences will be explained in thesectionon the architecture. ...
The neural network structure is derived from the U-Net architecture, described in this paper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Als...