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. · a fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations. It only contains convolutional layers and does not contain any dense layer because of which it can accept image of any size. Equivalently, an fcn is a cnn without fully connected layers. 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. Pleasant side effect of fcn is that they work on any spatial image size (bigger then receptive field) - … · a neural network that only uses convolutions is known as a fully convolutional network (fcn). There are different questions and even different lines of thought here. Fcnn is easily overfitting due to many params, then why didnt it reduce the params to reduce overfitting. Thus it is an end-to-end fully convolutional network (fcn), i. e. Lets go through them on resizing why do we need to resize? Here i give a detailed description of fcns and $1 \times 1$, which should also answer your question. If not, why do they perform as well as networks which use max-pooling? · 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. · there are mainly two main reasons for which we use fcn: In both cases, you dont need a squared image. 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. · why fully-connected neural network is not always better than convolutional neural network? To fit the network input which is fixed when nets are no fully convolutional networks (fcn) what if my net is fcn? · does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? · the second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions. Usually, the parameter cost of using a fully connected layer is high as compared to convolution layers. 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).
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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...
