What is the main difference between earlier and later filters in a CNN?

The main difference between earlier and later filters in a Convolutional Neural Network (CNN) lies in the nature of the features they learn. In the initial layers, the filters are designed to detect low-level features, which include basic elements like edges, corners, and textures. These foundational patterns are essential as they form the building blocks for more complex representations.

As the network progresses to the deeper layers, the filters start to capture higher-level abstractions. These later filters are capable of recognizing complex patterns or shapes that are made up of the low-level features identified by the earlier layers. For instance, while earlier layers might identify edges in an image, layers further into the network might combine those edges to identify shapes like faces or objects.

This hierarchical approach allows the network to build a sophisticated representation of the input data, making it effective in tasks such as image recognition, where understanding both the minute details and the larger context is crucial for accurate performance.