So, I've read about half the original ResNet paper, and am trying to figure out how to make my version for tabular data. I've read a few blog posts on how it works in PyTorch, and I see heavy use o...

Residual networks are always built with convolutional layers. I have never seen residual networks with only fully connected layers. Does it work to build a residual network with only fully connecte...

The Pytorch implementation of ResNet-18. has the following structure, which appears to be 54 layers, not 18. So why is it called "18"? How many layers does it actually have? ResNet ( (c...

I am reading through Residual learning, and I have a question. What is "linear projection" mentioned in 3.2? Looks pretty simple once got this but could not get the idea... Can some...

With the residual block in residual neural networks, is the addition at the end of the block true element addition or is it concatenation? For example, would addition([1, 2], [3, 4]) produce [1, 2...

This line works fine self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False) I introduced ResNet18 self.conv = ResNet18() **ResNet Class** '''ResNet in PyTorch. For Pre-ac...

I am a deep learning and Tensorflow beginner and I am trying to implement the algorithm in this paper using Tensorflow. This paper uses Matconvnet+Matlab to implement it, and I am curious if Tensor...

I would like to calculate constant convolution like blurring or resampling and want it never change during training. Can I initialize convolution kernel to constant and exclude it from training i...