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- What is the difference between a convolutional neural network and a . . .
A CNN, in specific, has one or more layers of convolution units A convolution unit receives its input from multiple units from the previous layer which together create a proximity Therefore, the input units (that form a small neighborhood) share their weights The convolution units (as well as pooling units) are especially beneficial as:
- machine learning - What is a fully convolution network? - Artificial . . .
A fully convolutional network is achieved by replacing the parameter-rich fully connected layers in standard CNN architectures by convolutional layers with $1 \times 1$ kernels I have two questions What is meant by parameter-rich? Is it called parameter rich because the fully connected layers pass on parameters without any kind of "spatial
- How to handle rectangular images in convolutional neural networks . . .
Almost all the convolutional neural network architecture I have come across have a square input size of an image, like $32 \\times 32$, $64 \\times 64$ or $128 \\times 128$ Ideally, we might not have a
- What is the computational complexity of the forward pass of a . . .
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- When training a CNN, what are the hyperparameters to tune first?
Firstly when you say an object detection CNN, there are a huge number of model architectures available Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak: Convolution Layer:- number of kernels, kernel size, stride length, padding
- In a CNN, does each new filter have different weights for each input . . .
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
- neural networks - How do we combine feature maps? CNN - Artificial . . .
In Convolutional Neural Networks we extract and create abstractified “feature maps” of our given image My thought was this: We extract things like lines initially Then from different types of lin
- How to use CNN for making predictions on non-image data?
You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment below) For example, in the image, the connection between pixels in some area gives you another feature (e g edge) instead of a feature from one pixel (e g
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