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A fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations My questions are why do we need to resize image. Equivalently, an fcn is a cnn without fully connected layers.
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A convolutional neural network (cnn) that does not have fully connected layers is called a fully convolutional network (fcn) In object detection, we can resize images by keeping the ratio the same as the original image, which is often known as "letterbox" See this answer for more info
Pooling), upsampling (deconvolution), and copy and crop operations.
The second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions It only contains convolutional layers and does not contain any dense layer because of which it can accept image of any size. I am trying to understand the pointnet network for dealing with point clouds and struggling with understanding the difference between fc and mlp Fc is fully connected layer operating on each p.
In the next level, we use the predicted segmentation maps as a second input channel to the 3d fcn while learning from the images at a higher resolution, downsampled by a factor of ds2 =ds1 /2, and optimized using dice loss l2. what does it mean by downsampling again by ds2? Fcnn is easily overfitting due to many params, then why didn't it reduce the params to reduce overfitting. I'm trying to replicate a paper from google on view synthesis/lightfields from 2019 View synthesis with learned gradient descent and this is the pdf
Basically the input to the neural ne.
The effect is like as if you have several fully connected layer centered on different locations and end result produced by weighted voting of them The difference between an fcn and a regular cnn is that the former does not have fully connected layers Therefore, fcns inherit the same properties of cnns There's nothing that a cnn (with fully connected layers) can do that an fcn.
