Transfer learning is a technique in machine learning where knowledge gained from training one model on one task is applied to another related task. It is particularly useful when dealing with limited labeled data. Here's how transfer learning helps in such situations:

  1. Feature Extraction: In transfer learning, the pre-trained model is used as a feature extractor. The lower-level layers of the pre-trained model capture general features such as edges, shapes, and textures, which can be useful for many different tasks. By using these pre-trained features, we can overcome the need for large labeled datasets for every specific task.

  2. Transfer of Knowledge: The learned representations from the pre-trained model contain valuable knowledge about the data, even if the model was trained on a different task or dataset. This knowledge can be effectively transferred to the target task with limited labeled data. By initializing the target model with the pre-trained weights, the model already starts with some understanding of the data, and it can converge faster with fewer labeled examples.

  3. Generalization: Transfer learning helps improve the generalization ability of the model. When training a model on limited labeled data, the risk of overfitting to that specific dataset is high. By using a pre-trained model, we leverage the knowledge learned from a large labeled dataset, which helps in capturing more general high-level features and reduces the risk of overfitting.

  4. Data Augmentation: Transfer learning allows us to use data augmentation techniques effectively. Data augmentation artificially increases the size of the labeled dataset by applying transformations such as rotation, scaling, and flipping. By leveraging a large pre-trained model, we can generate augmented data with diverse variations and thus increase the robustness and generalization capability of the model.

  5. Avoids Computational Cost: Collecting and labeling large amounts of data can be time-consuming and expensive. Transfer learning avoids this computational cost as it reuses the learned representations from the pre-trained model. We can focus our efforts on fine-tuning the model using the limited labeled data available, and achieve good performance even with less computational resources.

  In summary, transfer learning is a powerful technique for dealing with limited labeled data. By leveraging knowledge gained from pre-trained models, it helps overcome data scarcity, improves generalization, speeds up model convergence, leverages data augmentation effectively, and avoids the computational cost of collecting and labeling large datasets.