What is Transfer Learning?
Transfer learning is a machine learning technique where a model developed for a specific task is reused as the starting point for a model on a second task. This approach is especially useful when the second task has limited data available, allowing the model to leverage knowledge gained from the first task that typically has a larger dataset.
In practice, transfer learning often involves taking a pre-trained neural network, such as those used in image recognition (e.g., VGG, ResNet), and fine-tuning it on a new task by adjusting its weights. The basic intuition is that the deeper layers of these networks capture more abstract features, which can be applicable to various problems.
There are several methodologies for transfer learning, including:
- Fine-tuning: Unfreezing some of the top layers of the frozen model base and jointly training both the newly added classifier layers and the last few layers of the base model.
- Feature Extraction: Using the pre-trained model as a fixed feature-extractor and only training an upper classifier layer.
- Domain Adaptation: A specialized method that aligns the source (source task) and target (new task) domains more closely.
Transfer learning drastically reduces the time and resources spent on training models and often leads to improved performance, particularly in situations where data is scarce or costly to obtain.