Convolution Kernel Laboratory

Hand-design 3×3 convolution kernels — blurs, edge detectors, sharpeners — and see exactly what a CNN's first layer discovers automatically.

convolution
CNN
edge detection
filters

Original image

After convolution

Kernel weights

What CNNs learn

A trained CNN's first layer typically rediscovers exactly these shapes: edge detectors resembling Sobel kernels, blob detectors resembling Gaussian blur, and corner detectors formed by combining edges in multiple directions. A kernel whose weights sum to zero (Laplacian, Sobel) is a high-pass filter — it is silent on flat regions and fires only where intensity changes. The difference is that a CNN discovers these weights from data instead of a human designing them by hand.

Preparing the simulation…