DL0006 Layer Freeze in TL

Here are the common strategies for layer freezing in transfer learning:
(1) Freeze all but the output layer(s): Train only the final classification/regression layers. Good starting point for similar tasks and small datasets.
(2) Freeze early layers that capture general features: Train later, task-specific layers. Effective for moderately similar tasks. Balances leveraging pre-learned features with adapting higher-level representations.
(3) Fine-tune all layers with a low learning rate: Adapt all weights slowly. Use with caution on small datasets.
(4) Gradual Unfreezing: Start with frozen layers and progressively unfreeze layers during training to refine the model incrementally. Helps avoid large initial weight updates that can “destroy” learned features.