The Statistical Cost of Zero Padding in Convolutional Neural Networks (CNNs)

Zero padding is a fundamental technique in convolutional neural networks (CNNs) that involves adding zero-valued pixels around the borders of an input image. This approach enables convolutional kernels to process edge pixels effectively and helps maintain the spatial dimensions of feature maps, preventing excessive shrinking after multiple convolutional layers. By controlling the amount of padding, researchers and engineers can preserve important spatial information and facilitate the construction of deeper, more complex neural network architectures. Recent analyses highlight the trade-offs associated with zero padding, particularly its impact on the statistical cost and computational efficiency of CNNs. While zero padding allows for better feature