What type of features do early layers in a CNN typically learn?

Early layers in a Convolutional Neural Network (CNN) typically learn low-level features, and one of the primary types of features they focus on are edges. This includes gradients in the pixel intensity values, which help the network detect basic shapes and contours within an image. By recognizing these foundational elements, the CNN builds a hierarchical understanding of the data, progressively enabling the detection of more complex patterns as it moves towards deeper layers.

While low-level features encompass various characteristics such as colors and textures, the emphasis on edges is particularly critical because edges form the basis of the visual information processed by higher layers in the network. These early learned features are crucial for distinguishing between different parts of an image, laying the groundwork for the more sophisticated interpretations in later layers, which indeed start to learn shapes and more intricate components of the image.