· the second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions. To fit the network input which is fixed when nets are no fully convolutional networks (fcn) what if my net is fcn? In both cases, you dont need a squared image. Pleasant side effect of fcn is that they work on any spatial image size (bigger then receptive field) - … Lets go through them on resizing why do we need to resize? However, in fcn, you dont flatten the last convolutional layer, so you dont need a fixed feature map shape, and so you dont need an input with a fixed size. Here i give a detailed description of fcns and $1 \times 1$, which should also answer your question. 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. The difference between an fcn and a regular cnn is that the former does not have fully connected layers. Fcnn is easily overfitting due to many params, then why didnt it reduce the params to reduce overfitting. See this answer for more info. It only contains convolutional layers and does not contain any dense layer because of which it can accept image of any size. There are different questions and even different lines of thought here. Theres nothing that a cnn (with fully connected layers) can do that an 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). · a neural network that only uses convolutions is known as a fully convolutional network (fcn). Therefore, fcns inherit the same properties of cnns. · there are mainly two main reasons for which we use fcn: Equivalently, an fcn is a cnn without fully connected layers. · a fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations. You just have to be careful in the case you use cnn with a fully connected layer, to have the right shape for the flatten layer. Usually, the parameter cost of using a fully connected layer is high as compared to convolution layers. · 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. Thus it is an end-to-end fully convolutional network (fcn), i. e. · for example, u-net has downsampling (more precisely, max-pooling) operations. · why fully-connected neural network is not always better than convolutional neural network?
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· the second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions. To...
