How Does Transfer Learning Work?
Transfer learning is a machine learning technique that enables a model trained on one task to be adapted for a different but related task. This approach leverages existing knowledge (the "source" task) to improve learning efficiency and performance on a new task (the "target" task).
1. Pre-trained Models
Transfer learning typically starts with a pre-trained model, which has already been trained on a large dataset, such as ImageNet for image classification. These models learn fundamental features (like edges, shapes, textures) that are generally useful across different tasks.
2. Feature Extraction
In this step, the pre-trained model acts as a feature extractor. The lower layers of the neural network capture general features, while higher layers can be fine-tuned to specific features relevant to the target task. The final classification layer is usually replaced with a new one suited for the new task.
3. Fine-Tuning
Fine-tuning involves training the modified model on the target dataset. During this process, some layers of the network may be frozen (kept as they are) while the new layers are trained. This allows for faster convergence and increased accuracy since the model utilizes previously learned features.
4. Advantages
Transfer learning significantly reduces training time and requires less data to achieve high performance in the target task. It's particularly useful in domains where labeled data is scarce.
5. Applications
With its efficiency, transfer learning finds applications in various fields like computer vision, natural language processing, and speech recognition, making it a key methodology in deep learning and artificial intelligence.