Fine-tuning and feature extraction are two common approaches in transfer learning, which involves leveraging pre-trained deep learning models for new tasks. While both methods aim to adapt a pre-trained model to a new task, there are differences in the strategies and procedures involved.

  1. Definition and Concept:

   - Fine-tuning: Fine-tuning involves taking a pre-trained model and further training it on a new dataset specific to the target task. The objective is to update the weights of the pre-trained model's layers to better fit the new data.

   - Feature extraction: Feature extraction involves using a pre-trained model as a fixed feature extractor, where the pre-trained layers are frozen and acts as a fixed feature representation. A new classifier or regressor is then trained on top of these extracted features.

  2. Training Procedure:

   - Fine-tuning: In fine-tuning, the entire neural network, including pre-trained layers and newly added layers, is trained together. The weights of the pre-trained layers are updated during backpropagation based on the new data, while the newly added layers may have randomly initialized weights or may be initialized with the pre-trained weights and fine-tuned accordingly.

   - Feature extraction: In feature extraction, only the added layers on top of the pre-trained model are trained. The pre-trained layers are kept frozen, acting purely as a feature extractor. The weights of the pre-trained layers are not updated during training.

  3. Data Efficiency:

   - Fine-tuning: Since fine-tuning allows the pre-trained model to be trainable on new data, it can potentially achieve higher accuracy compared to feature extraction. Fine-tuning relies on both the pre-trained weights and the new data to adjust the model's parameters.

   - Feature extraction: Feature extraction requires fewer new data samples as it only trains the added layers. This can be advantageous with limited labeled data, as the pre-trained model already has learned useful hierarchical features from a large dataset.

  4. Overfitting Robustness:

   - Fine-tuning: Fine-tuning can be prone to overfitting on small datasets, especially if the new task has significantly different characteristics from the original pre-training task. Regularization techniques like dropout and early stopping can be used to mitigate this.

   - Feature extraction: Feature extraction is less prone to overfitting since the pre-trained layers are fixed. However, overfitting can still occur in the newly added layers if the amount of new data is limited.

  5. Training Time and Computational Resources:

   - Fine-tuning: Fine-tuning typically requires more computational resources and training time compared to feature extraction. Since the entire network is trained, the backpropagation process needs to update a larger number of parameters.

   - Feature extraction: Feature extraction is computationally less expensive as it only requires training the added layers, which usually have fewer parameters than the pre-trained layers.

  In summary, the choice between fine-tuning and feature extraction depends on factors such as the available labeled data, computational resources, and similarity between the pre-training task and the target task. Fine-tuning is more flexible and can potentially achieve higher accuracy, while feature extraction is generally efficient when labeled data is limited.