What is input depth in CNN?

7. 13. In Deep Neural Networks the depth refers to how deep the network is but in this context, the depth is used for visual recognition and it translates to the 3rd dimension of an image. In this case you have an image, and the size of this input is 32x32x3 which is (width, height, depth) .

What is the role of filters in CNN?

In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image.

What are filter sizes in CNN?

Smaller kernel sizes consists of 1×1, 2×2, 3×3 and 4×4, whereas larger one consists of 5×5 and so on, but we use till 5×5 for 2D Convolution. In 2012, when AlexNet CNN architecture was introduced, it used 11×11, 5×5 like larger kernel sizes that consumed two to three weeks in training.

Why filters are used in CNN?

Why does the number of filters increase as the CNN gets deeper?

Once the useful features have been extracted, then we make the CNN elaborate more complex abstractions on it. That is why the number of filters usually increases as the Network gets deeper, even though it doesn’t necessarily have to be like that. Share Improve this answer Follow

How many weights per input channel for a CNN?

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.

How to increase the size of the output image without decreasing?

Normally, the width of the output gets smaller, just like the size of the output in 2D case. If you want to keep the output image at the same width and height without decreasing the filter size, you can add padding to the original image with zero’s and make a convolution slice through the image. We can apply more padding!

Why do we increase the filter size in subsequent layers?

Now as we move forward in the layers, the patterns get more complex; hence there are larger combinations of patterns to capture. That’s why we increase the filter size in subsequent layers to capture as many combinations as possible. Share Improve this answer Follow edited May 1 at 9:14