How Does Transfer Learning Work?
Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task. This approach is especially beneficial in deep learning, where training a model from scratch can be computationally expensive and time-consuming.
The primary idea behind transfer learning is to leverage knowledge acquired from a source task to improve performance or efficiency on a target task. This is particularly useful when the target task has limited data. In most scenarios, a pre-trained model (often trained on a large dataset, such as ImageNet) is fine-tuned on the smaller dataset related to the target task.
There are various strategies to implement transfer learning:
- Feature Extraction: Utilize the learned features from the pre-trained model while training the top layers on the target dataset.
- Fine-tuning: Unfreeze some of the top layers of the pre-trained model and jointly train them alongside the newly added layers but with a smaller learning rate.
- Domain Adaptation: Adjust the model to improve performance on a related but different task or domain.
Overall, transfer learning accelerates the training process, often leading to better performance with fewer data, thus it has become a standard technique in numerous applications, including computer vision and natural language processing.