Transfer learning in neural networks is a technique where a model developed for one task is reused as the starting point for a different but related task. Instead of training a new model from scratch, you take a pre-trained model—usually trained on a large dataset—and adapt it to your specific problem. This approach saves computational resources, reduces training time, and often improves performance, especially when working with limited data. The core idea is that the knowledge gained from solving the original task (like recognizing objects in images) can be transferred to a new task (like detecting medical anomalies in X-rays), leveraging patterns the model has already learned.
A common example is using pre-trained image classification models like ResNet or VGG16, which are trained on datasets like ImageNet. Developers can remove the final classification layer of these models and replace it with new layers tailored to their specific task. For instance, a model trained on general images can be adapted to classify bird species by retraining only the new layers on a smaller dataset of bird photos. The earlier layers, which detect basic features like edges or textures, remain largely unchanged because these low-level features are useful across many visual tasks. This process, called fine-tuning, allows the model to build on existing knowledge rather than relearning fundamentals, making it efficient for specialized applications.
Transfer learning works best when the original and new tasks share underlying patterns. For example, natural language processing models like BERT are often repurposed for sentiment analysis or text classification by retraining the top layers on domain-specific text data. However, if the tasks are too dissimilar—like using an image model for audio processing—the benefits diminish. Developers should also consider freezing some layers during retraining to prevent overfitting, especially with small datasets. While transfer learning isn’t a universal solution, it’s a practical tool for leveraging existing models to solve new problems efficiently, balancing customization with resource constraints.