Transfer learning is a technique used in machine learning that allows developers to utilize knowledge gained from a pre-trained model on a large dataset to improve performance on a smaller dataset. When you have limited or no data available for training a model from scratch, transfer learning can help you accelerate the training process and achieve better accuracy. Essentially, you are leveraging the model's existing understanding of features learned from a different but related task, allowing it to adapt to your specific problem with less data.
To implement transfer learning, you typically start with a pre-trained model, which has already been trained on a large dataset. Popular examples include models like VGG, ResNet, or BERT, depending on whether you are dealing with images or text data. The first step is to modify the model by replacing the output layer to fit your specific classification or regression task. For instance, if you are working on a new image classification problem with ten classes, you would replace the final layer of a model pre-trained on a larger dataset (like ImageNet) to reflect those ten classes.
After modifying the model, you can proceed to fine-tune it on your limited dataset. You can freeze some of the initial layers of the network, which retain general feature extraction capabilities, and only train the final layers, allowing the model to specialize in your specific task. This approach reduces the risk of overfitting, which is a common issue when working with small datasets. By using transfer learning in this way, developers can significantly enhance model performance without the need for extensive data collection efforts.