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Transfer learning is a machine learning technique where a model trained on one task is adapted to perform a different, but related, task. Instead of training a model from scratch for a new task, transfer learning leverages knowledge gained from solving a source task to improve performance on a target task. This is particularly useful when labeled data for the target task is limited, expensive, or difficult to obtain.
The typical transfer learning process involves the following steps:
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Pre-training: A model is first trained on a large dataset for a source task. This source task is often chosen because it shares some underlying features or representations with the target task. The model learns general features and patterns during this pre-training phase.
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Feature Extraction or Fine-tuning: After pre-training, the knowledge gained by the model is transferred to the target task. There are two common approaches:
- Feature Extraction: The pre-trained model is used as a fixed feature extractor. The weights of the earlier layers are frozen, and only the weights of the later layers are fine-tuned on the target task. This is particularly common in tasks like image classification.
- Fine-tuning: The entire pre-trained model is further trained on the target task, allowing all layers to be adjusted based on the new data. This is more common in cases where the target task has a large amount of labeled data.
Transfer learning offers several advantages, including:
- Reduced Training Time: Since the model starts with pre-learned features, it typically requires less time and resources to adapt to the target task.
- Improved Performance: Transfer learning can lead to better performance on the target task, especially when there is a limited amount of labeled data for that task.
- Effective Use of Pre-trained Models: Models pre-trained on large datasets (e.g., ImageNet for image-related tasks) can be leveraged for a wide range of downstream tasks.
Transfer learning is widely used in various domains such as computer vision, natural language processing, and speech recognition, where pre-trained models on large datasets like ImageNet or language models like BERT have demonstrated significant utility for diverse tasks.