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title Padding in CNNs
sidebar_label Padding
description How padding prevents data loss at the edges and controls the output size of convolutional layers.
tags
deep-learning
cnn
computer-vision
padding
zero-padding

When we slide a kernel over an image in a Convolutional Layer, two problems occur:

  1. Shrinking Output: The image gets smaller with every layer.
  2. Loss of Border Info: Pixels at the corners are only "touched" by the kernel once, whereas central pixels are processed many times.

Padding solves both by adding a border of extra pixels (usually zeros) around the input image.

1. The Border Problem

Imagine a $3 \times 3$ kernel sliding over a $5 \times 5$ image. The center pixel is involved in 9 different multiplications, but the corner pixel is only involved in 1. This means the network effectively "ignores" information at the edges of your images.

2. Types of Padding

There are two primary ways to handle padding in deep learning frameworks:

A. Valid Padding (No Padding)

In "Valid" padding, we add zero extra pixels. The kernel stays strictly within the boundaries of the original image.

  • Result: The output is always smaller than the input.
  • Formula: $O = (W - K + 1)$

B. Same Padding (Zero Padding)

In "Same" padding, we add enough pixels (usually zeros) around the edges so that the output size is exactly the same as the input size (assuming a stride of 1).

  • Result: Spatial dimensions are preserved.
  • Common use: Deep architectures where we want to stack dozens of layers without the image disappearing.

3. Mathematical Formula with Padding

When we include padding ($P$), the formula for the output dimension becomes:

$$ O = \frac{W - K + 2P}{S} + 1 $$

  • $W$: Input dimension
  • $K$: Kernel size
  • $P$: Padding amount (number of pixels added to one side)
  • $S$: Stride

:::note For "Same" padding with a stride of 1, the required padding is usually $P = \frac{K-1}{2}$. This is why kernel sizes are almost always odd numbers ($3 \times 3, 5 \times 5$). :::

4. Other Padding Techniques

While Zero Padding is the standard, other methods exist for specific cases:

  • Reflection Padding: Mirrors the pixels from inside the image. This is often used in style transfer or image generation to prevent "border artifacts."
  • Constant Padding: Fills the border with a specific constant value (e.g., gray or white).

5. Implementation

TensorFlow / Keras

Keras simplifies this by using strings:

from tensorflow.keras.layers import Conv2D

# Output size will be smaller than input
valid_conv = Conv2D(32, (3, 3), padding='valid')

# Output size will be identical to input
same_conv = Conv2D(32, (3, 3), padding='same')

PyTorch

In PyTorch, you specify the exact number of pixels:

import torch.nn as nn

# For a 3x3 kernel, padding=1 gives 'same' output
# (3-1)/2 = 1
conv = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, padding=1)

References

Padding keeps the image size consistent, but what if we want to move across the image faster or purposely reduce the size?