Frozen weights refer to specific parameters in a machine learning model that have been set to remain constant during the training process. This means that these weights are not updated or modified as the model learns from the training data. The concept of freezing weights is commonly employed in transfer learning, where a model that has been pre-trained on a large dataset is adapted for a new, often smaller dataset.
In transfer learning, certain layers of the neural network may have their weights frozen to retain the learned features from the original task. This allows the model to leverage its pre-existing knowledge while adapting to the new task without overfitting to the limited data available. For instance, in convolutional neural networks (CNNs) used for image classification, the earlier layers might be frozen to preserve the general feature detectors (like edges and textures) learned from the vast original dataset, while the later layers can be fine-tuned to specialize in the new dataset.
Freezing weights can also speed up training, as fewer parameters need to be updated, thus reducing computational expense. However, it is important to choose which weights to freeze carefully, as freezing too many parameters can hinder the model’s ability to adapt to new tasks effectively. Balancing frozen and trainable weights is key to optimizing model performance.