Deep Learning Tutorial: Padding in Convolutional Neural Networks

Introduction

• Presenter: Krishnag
• Focus: Padding in Convolutional Neural Networks (CNNs)
• Continuation from a previous video on convolution operations.

Review of Convolution Operation

• Example:
  • Image: 6x6 pixels
  • Filter: 3x3 vertical edge filter
  • Output: 4x4 pixels
• Convolution uses strides to jump and apply the filter, resulting in smaller output dimensions.

Problem Identified

• Loss of data when convolving: 6x6 input to 4x4 output.
• Need to maintain original image size after convolution.

Introduction to Padding

• Padding is used to retain the original dimensions of an image after convolution.
• Formula for output dimension without padding: N - F + 1
  • Example: N = 6, F = 3 results in output 4.
• To retain original size (e.g., 6x6), calculate the padding needed.

Calculating Padding

• Adjusted formula with padding: N + 2P - F + 1
• Example Calculation:
  • Given: N = 6, F = 3, P = 1
  • Formula: 6 + 2(1) - 3 + 1 = 6
• Padding of 1 results in expanding 6x6 to 8x8 (by adding rows and columns).

Types of Padding

1. Zero Padding:
   • Fill added rows and columns with zeros.
   • Most common practice.
2. Value-based Padding:
   • Fill with nearest pixel values.

Benefits of Padding

• Retains original image size, allowing multiple convolutions without data loss.
• Enhances edge detection due to added boundary conditions.

Conclusion

• Padding ensures no loss of information and allows deeper network architectures by maintaining consistent image sizes.
• Encourages viewers to apply padding in CNNs for better feature extraction.

Call to Action

• Encourages viewers to subscribe for more tutorials.
• Promises further exploration of CNNs and deep learning concepts in upcoming videos.

These notes summarize key concepts from the lecture on padding in CNNs, explaining why and how padding is implemented to maintain image dimensions and data integrity.