Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 9378–9387. [Google Scholar] Ullah, A.; Muhammad, K.; Haq, I.U.; Baik, S.W...
Schematic structure of the used residual block is shown in Fig.2. The parameter settings of the residual groups are shown in Table2. As can be seen from Fig.2, the first layer and the third layer of the residual block adopt the 1 × 1 × 1 convolution kernel, and the middle...
block, which reduces the four-dimensional space to a single continuous linear vector. The output of the flattening layer is fed into a fully connected layer or dense layer. Our architecture uses two fully connected layers with 512 and 256 neurons, respectively. The output layer or Softmax ...
Each adjacent 3D block originates from the PCA-reduced hyperspectral data cube, and they are uniquely identified by the central spatial coordinates. We denote each 3D block as 𝐼𝑏𝑙𝑜𝑐𝑘Iblock∈ℝ𝑠×𝑠×𝐾∈Rs×s×K, where s represents the size of each spatial window and...
在下面的例子中,我们定义一个有 2 个输出通道数为 10 的 `DenseBlock`。 在下面的例子中,我们[**定义一个**]有 2 个输出通道数为 10 的 (**`DenseBlock`**)。 使用通道数为 3 的输入时,我们会得到通道数为 $3+2\times 10=23$ 的输出。 卷积块的通道数控制了输出通道数相对于输入通道数的增长,...
(a), ResNet-50 (b), and DenseNet-169 (c). VGG-13 includes several convolution and pooling layers as the building block of the architecture. The ResNet-50 has two types of residual blocks building the entire network. Alternatively, DenseNet-169 consists of dense and transition blocks as ...
Afterwards, there were three dense blocks and two transition layers in the proposed 3D DenseNet model. Each dense block was composed by a set of dense units, where a dense unit consisted of a 1 × 1 × 1 conv and a 3 × 3 × 3 conv. The 1 × 1 × 1 convolution was employed as...
Each adjacent 3D block originates from the PCA-reduced hyperspectral data cube, and they are uniquely identified by the central spatial coordinates. We denote each 3D block as 𝐼𝑏𝑙𝑜𝑐𝑘Iblock∈ℝ𝑠×𝑠×𝐾∈Rs×s×K, where s represents the size of each spatial window and...
In Figure 6, a block diagram of the proposed framework is shown. Figure 6. Proposed framework. 3.1. Problem Statement Given 𝒳∈ℝ𝐼1×𝐼2×𝐼3X∈RI1×I2×I3, which is a source HSI in a third-order tensor form, assuming high-SNR, and 𝐘∈ℕ𝐼1×𝐼2Y∈NI1×I2, ...
Figure 5. The spatial residual block consists of two successive 3D CNN layers, and a skip connection to add input feature maps 𝐴𝑟Ar directly to the output feature maps 𝐴𝑟+2Ar+2. There are five 3D convolutional layers, including an initial layer followed by two residual blocks; ...