· 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 … Here i give a detailed description of fcns and $1 \times 1$, which should also … · there are mainly two main reasons for which we use fcn: · for example, u-net has downsampling (more precisely, max-pooling) operations. Pleasant side effect of fcn is … In both cases, you dont need a … Lets go through them on resizing why do we need to resize? · 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. If we use a fully connected layer for any classification or regression task, we have to flatten the results before … · why fully-connected neural network is not always better than convolutional neural network? · a fully convolution network (fcn) is a neural network that only performs convolution (and subsampling or upsampling) operations. The difference between an fcn and a regular cnn is that the former does not have fully … Equivalently, an fcn is a cnn … Thus it is an end-to … · 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 … To fit the network input which is fixed when nets are no … · the second path is the symmetric expanding path (also called as the decoder) which is used to enable precise localization using transposed convolutions. 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.
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· in the next level, we use the predicted segmentation maps as a second input channel to the 3d fcn while learning from the images...
