- By applying the convolutional filter every time, the original image sinks. i.e. the output image has smaller dimensions than the original input image which may lead to information loss.
- Pixels at the corner of the image used in only one of the outputs than pixels in the middle which lead to huge information loss.
ML | Introduction to Strided Convolutions
Let us begin this article with a basic question – “Why padding and strided convolutions are required?”
Assume we have an image with dimensions of n x n. If it is convoluted with an f x f filter, then the dimensions of the image obtained are .
Example:
Consider a 6 x 6 image as shown in figure below. It is to be convoluted with a 3 x 3 filter. The convolution is done using element wise multiplication.
Figure 1: Image obtained after convolution of 6×6 image with a 3×3 filter and s=0
Figure 2: 6 x 6 filterFigure 3: 3 x 3 filterFigure 4: Element wise multiplication
But there are two downsides of this convolution:
The stride amount should be selected such that comparatively lesser computations are required and the information loss should be minimum.
Figure 5: Image obtained after convolution of 6×6 image with a 3×3 filter and a stride of 2
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