MSN: FC Schalke 04 - 1. FC Nürnberg im Live-Stream und TV: FCN am 14.12.2025 mit harter Aufgabe gegen die Knappen
FC Schalke 04 - 1. FC Nürnberg im Live-Stream und TV: FCN am 14.12.2025 mit harter Aufgabe gegen die Knappen
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.
For questions related to fully convolutional networks (FCNs), which is formally described in the paper "Fully Convolutional Networks for Semantic Segmentation" (2015) by Jonathan Long et al. An example of an FCN is the U-net (introduced in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger et al.).
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-to-end fully convolutional network (FCN), i.e. it only contains Convolutional layers and does not contain any Dense layer because of which it can accept image of any size.
A neural network that only uses convolutions is known as a fully convolutional network (FCN). Here I give a detailed description of FCNs and $1 \times 1$, which should also answer your question.