However, Convolution Neural Network (CNN) is one of the most important networks that have been used especially in image classification, these networks are facing an essential problem which is the "overfitting problem". This problem means that the difficulty implies that t...
Universal Approximation Theorem says that Feed-Forward Neural Network (also known as Multi-layered Network of Neurons) can act as powerful approximation to learn the non-linear relationship between the input and output. But the problem with the Feed-Forw
Now we know how to use transpose convolution to up-samples an image. When you are training a neural network we need to figure out the values in filters of transpose convolution layers, same as in CNN. That’s where our friend backpropagation comes to help.Thanks...
To overcome this problem, ResNet utilizes residual blocks, which allow for skip connections between blocks of convolutional layers. These skip connections enhance gradient propagation and facilitate the training of increasingly deeper CNNs, mitigating the issue of gradient vanishing. A residual layer can...
Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Convolution Layer (GRCL), is constructed by adding a gate ...
It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limited-angle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to ...
the output dimension will always be smaller than input. However if we want to build a deep convolution network, we don't want the input size to shrink too fast. A small kernel can partly solve this problem. But in order to maintain certain dimension we need zero padding. Basically it is...
One problem with convolutions is that we tend tolose pixelsand information on the perimeter of the image. This is down to how many times they are utilised by the kernel. The corner pixels will only ever get used once, whilst the middle pixels get used a lot more. ...
Build and train a ConvNet in TensorFlow for a classification problem We assume here that you are already familiar with TensorFlow. If you are not, please refer theTensorFlow Tutorialof the third week of Course 2 (“Improving deep neural networks”). ...
which calculates the whole image, will produce a saliency image of uniform density. However, the computational complexity increases using the global image processing. And if reducing the resolution or reducing the feature dimension is applied to address the problem, it leads to minutiae deletion and...