DenseNet Architecture Explained with PyTorch Implementation from TorchVision
虽然每个层输出k个feature map, 但是通常每个层有更多的输入。在文献[36]Rethinking the inception architecture for computer vision. InCVPR, 2016.,文献[11] [Deep residual learning for image recognition.In CVPR, 2016], 在每个3x3之前,引入1x1卷积作为瓶颈层(bottleneck layer)来减少输入feature map的数量,因...
This reduction is beneficial to minimize neural network training time and storage needs of our development explained in the next section. 4.3 Our Development We chose to use a convolutional DNN of densenet-BC architecture because of our objective to use the least resources possible. This kind of ...
2.5. Architecture of Convolutional Neural Networks A generalized CNN contains a convolution layer, a pooling layer, and a fully connected (FC) layer. Among them, the convolutional layer, which is the core of the convolutional model, serves to automatically extract image features [31]. Pooling lay...