How do CNNs fundamentally transform data during processing?

Convolutional Neural Networks (CNNs) fundamentally transform data during processing by applying filters to extract features. This process involves convolving the input data with a set of learnable filters, also known as kernels. These filters slide over the input data, typically images, and capture various features such as edges, textures, and patterns at different spatial hierarchies.

As the CNN processes the data through multiple layers, it learns increasingly complex features, allowing it to effectively recognize objects or patterns within the images. By focusing on local regions of the input and combining these features, CNNs can maintain spatial relationships and reduce the dimensionality of input data while retaining important information, which is crucial for tasks such as image classification and object detection.

This feature extraction process is what enables CNNs to excel in handling visual data, making them a fundamental architecture in the field of computer vision. The other options do not accurately encompass the mechanism by which CNNs operate, as they either suggest incorrect methods of transformation or reference processes not relevant to CNN functionality.