Adjusting Image Size with Padding

In CNNs, padding refers to the process of inserting additional pixels at the edges of the input image during convolution operations.

Padding allows the image size to be maintained or adjusted after convolution, ensuring important information is preserved as the network gets deeper.

Convolution operations cause a reduction in output size as filters move over the image.

When processing small images, repeated convolutions can rapidly reduce the size, risking loss of important details.

Using padding can effectively address these issues, providing benefits such as:

1. Preserving the original image size to retain information in deep networks.

2. Allowing edge pixels to be included in the convolution operation, enabling better feature representation.

In CNNs, the most common type of padding is Same Padding, where 0 values are added to the edges.

Same Padding helps smaller images retain their features and preserves information as the model becomes deeper.

For small images, Same Padding is typically used to maintain size, while Valid Padding may be preferred for larger images to reduce computation.

In the next lesson, we will explore pooling, a technique often employed in conjunction with padding.