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-training, they are largely transferable to many new image tasks.

Later layers, or deeper layers, combine these low-level features to recognize more complex, high-level, and abstract patterns. These can include object parts (like an eye or a wheel) or even complete objects (like a face or a whole vehicle). The very last layers are typically responsible for making final predictions based on these high-level features, adapting them to specific classes or outcomes.

Freezing the first few layers means that their weights, which are the parameters that define the learned features, are kept constant and are not updated during the subsequent training on the new task. This is done for several key reasons:

First, the low-level features learned by these early layers are already highly effective and universal. Retraining them on a potentially smaller or more specific new dataset could degrade their quality or cause the model to 'forget' these robust general features, a phenomenon known as catastrophic forgetting. By freezing them, the model retains this valuable, generalized knowledge.

Second, freezing layers significantly reduces the number of trainable parameters in the model. This leads to substantial computational efficiency, requiring less memory and enabling faster training times, which is particularly beneficial when computational resources are limited or when iterating quickly on new tasks.

Third, it acts as a form of regularization. When the new task's dataset is relatively small, training all layers from scratch or unfreezing too many layers can lead to overfitting. Overfitting occurs when the model learns the training data too specifically, including noise, and performs poorly on unseen data. By keeping the early, generalizable feature extractors fixed, the model is constrained to adapt only its higher-level representations, which helps prevent it from overly specializing to the limited new data.

Conversely, the later layers are trained because their learned features are more abstract and task-specific. While the early layers provide a strong foundation of generic visual understanding, the new task requires the model to adapt its high-level feature extraction and decision-making processes to its unique requirements. For example, if the pre-trained model was for general object recognition and the new task is to classify specific dog breeds, the later layers need to learn to distinguish subtle differences between breeds, using the foundational features provided by the frozen early layers. The final output layer, in particular, almost always needs to be completely retrained or replaced to match the specific number and nature of the classes or outputs for the new task.