machine learning - What is a fully convolution network? - Artificial . . . Fully convolution networks 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 Convolution neural networks The typical convolution neural network (CNN) is not fully convolutional because it often contains fully connected layers too (which do not perform the
How do I handle large images when training a CNN? Suppose that I have 10K images of sizes $2400 \\times 2400$ to train a CNN How do I handle such large image sizes without downsampling? Here are a few more specific questions Are there any tech
convolutional neural networks - In a CNN, does each new filter have . . . Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel So the diagrams showing one set of weights per input channel for each filter are correct
machine learning - How do neural networks learn specific features . . . That convolution responds to certain arrangements of these 1st-level features, e g two adjacent edges with different orientations are a corner You can think of the CNN-layers as a hierarchy where initial layers provide basic features the next layer detects compositions of these, the next layer detects compositions of the compositions and so on