Convolution Layers

Understanding Convolution Layers

Convolution layers are the core of Convolutional Neural Networks (CNNs). They apply convolution, where a small matrix called a filter (or kernel) slides over an image to detect edges, textures, and shapes. This allows CNNs to process images more efficiently than traditional networks.

Instead of analyzing an entire image at once, CNNs break it into smaller sections, detecting features at different levels. Early layers recognize simple patterns like edges, while deeper layers detect complex structures.

How Convolution Works

Convolution involves a filter (kernel) moving across an image, following these steps:

1. Apply the kernel at the top-left of the image.
2. Perform element-wise multiplication between the kernel and pixel values.
3. Sum the products to generate an output pixel.
4. Move the kernel according to the stride and repeat.
5. Generate a feature map that highlights detected patterns.

Multiple filters help CNNs capture different features, such as vertical edges, curves, and textures.

Filters (Kernels):

Filters play a crucial role in extracting meaningful patterns from images. Different types of filters specialize in identifying various features:

• Edge detection filters – identify object boundaries by detecting abrupt intensity changes (e.g., Sobel, Prewitt, and Laplacian filters).

• Texture filters – capture repetitive patterns such as waves or grids (e.g., Gabor filters).

• Sharpening filters – enhance image details by amplifying high-frequency components.

• Blurring filters – reduce noise and smooth images (e.g., Gaussian blur filter).

• Emboss filters – highlight edges and add a 3D effect by emphasizing depth.

Each filter is trained to detect specific patterns and contributes to building hierarchical feature representations in deep CNNs.

Convolution layers reuse the same filter across an image, reducing parameters and making CNNs efficient. However, specialized locally connected layers use different filters for different regions when needed.

By stacking convolution layers, CNNs extract detailed patterns, making them powerful for image classification, object detection, and vision tasks.

Hyperparameters:

• Stride – controls how far the filter moves per step.
• Padding – adds pixels to control output size (same padding preserves size, valid padding reduces it).
• Number of Filters (Depth) – more filters improve feature detection but increase computation.

Before the next chapter, you need to remember:

Although convolutional layers can decrease output size, their primary purpose is feature extraction, not dimensionality reduction. Pooling layers, on the other hand, explicitly reduce dimensionality while retaining important information, ensuring efficiency in deeper layers.