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A fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations

Equivalently, an fcn is a cnn without fully connected layers. There are mainly two main reasons for which we use fcn If we use a fully connected layer for any classification or regression task, we have to flatten the results before transferring the information into the fully connected layer, which will result in the loss of spatial information Usually, the parameter cost of using a fully connected layer is high as compared to convolution layers. Mlp, is grounded in symmetry For example, a cnn responds very naturally to image translations

This behavior is called translational equivariance which arises from shifting the same weights (i.e 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. 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? 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.

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 See this answer for more info Therefore, fcns inherit the same properties of cnns There's nothing that a cnn (with fully connected layers) can do that an fcn. There are different questions and even different lines of thought here Let's go through them on resizing why do we need to resize

To fit the network input which is fixed when nets are no fully convolutional networks (fcn) what if my net is fcn Still makes sense to resize to bound the dimension of the input features you want to detect (a person on a small image vs big image) What prompt (or other technique) should i use with an llm so that the result is guaranteed to be reliably parseable as a list of values (e.g A python list of strings) llm would understand that a