When using a very powerful pre-trained image model for a new task, why might an expert freeze the first few layers and only train the later ones?

When using a very powerful pre-trained image model, an expert freezes the first few layers and only trains the later ones due to the nature of how deep neural networks, particularly convolutional neural networks (CNNs), learn and the principles of transfer learning. A pre-trained image model is a deep learning model that has already been extensively trained on a massive and diverse dataset, such as ImageNet, to perform a general image recognition task. This pre-training allows the model to learn a hierarchical representation of visual features across its layers. In a CNN, the layers are organized sequentially, with each layer learning increasingly complex features. The first few layers, often called early layers, are responsible for detecting fundamental, low-level features that are generic across almost all images, regardless of their content. These include basic visual elements such as edges, corners, textures, and color blobs. For instance, whether the model is identifying a cat or a car, the initial step of recognizing an edge is universal. Because these features are highly generalized and robustly learned from a vast dataset during pre-tra....