What is Transfer Learning?
Transfer learning is a machine learning technique that leverages knowledge gained from one task to improve performance on a related task. In the context of deep learning, it involves pre-training a model on a large dataset and then fine-tuning it on a smaller, task-specific dataset. This approach is particularly beneficial when labeled data is scarce or expensive to obtain.
A typical transfer learning workflow consists of two main stages:
- Pre-training: A model, often a deep neural network, is initially trained on a general dataset (commonly known as the source domain). For example, models like VGG, ResNet, or BERT are trained on large datasets like ImageNet or large text corpuses. This enables the model to learn a broad range of features or representations.
- Fine-tuning: After pre-training, the model is further refined or adjusted on a smaller, more specific dataset (known as the target domain). This fine-tuning process allows the model to adapt its learned features to the specific characteristics of the new dataset, improving accuracy and efficiency.
Transfer learning is particularly popular in areas such as image classification, natural language processing, and speech recognition. By utilizing pre-trained models, practitioners can save time and resources, allowing them to build powerful models even with limited data.
Overall, transfer learning represents a valuable approach in the realm of deep learning, enhancing the capability and effectiveness of AI applications across various domains.