machine learning - What is a fully convolution network? - Artificial ...
Jun 12, 2020 · A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an FCN is a CNN …
Are fully connected layers necessary in a CNN?
Aug 6, 2019 · 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 …
Why can a fully convolutional network accept images of any size?
Jun 27, 2019 · The second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions. Thus it is an end …
What is the difference between FC and MLP in as used in PointNet?
Apr 20, 2020 · 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 …
Why FCNN is not always better than CNN?
Feb 17, 2023 · Why Fully-Connected Neural Network is not always better than Convolutional Neural Network? The main reason why in many cases, a CNN will outperform a fully …
What does 'downsampling' and 'upsampling' mean in coarse-to …
May 31, 2021 · 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 …
Wouldn't convolutional neural network models work better …
Nov 12, 2019 · 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. Pleasant side effect of FCN is …
Does a fully convolutional network share the same translation ...
Feb 21, 2020 · For example, u-net has downsampling (more precisely, max-pooling) operations. The difference between an FCN and a regular CNN is that the former does not have fully …
Why do we do need compression in Semantic Segmentation?
When doing semantic segmentation, we often make use of FCN, which can be thought of in two parts: an encoder and decoder. As I understand, the encoder compresses the image into a …
Why do we resize images before using them for object detection?
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 …