Guide to Different Padding Methods for CNN Models

Convolutional neural networks(CNNs) are used every day for tackling various problems occurring in image processing and predictive modelling or classification tasks. The most popular application for CNNs is to analyze image data. As we know mathematically, every image in any dataset is a matrix of its pixel values. When working with the simple CNN for an image, we get the output reduced in size which we can definitely consider as the loss of data. And sometimes it becomes very difficult to generate a proper result according to our requirements. In that case, where we don't want the shape or our outputs to reduce in size, the addition of more layers in the data can help and that addition can be done by padding.  In this article, we will discuss padding with its importance and how to use