Transfer learning involves adapting a pretrained model to a new task. Here are the general steps of transfer learning:
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Select a pretrained model: Choose a pretrained model that was trained on a large dataset for a similar task. Common choices include models pretrained on ImageNet for computer vision tasks or models like BERT for natural language processing.
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Add specific layers: Introduce new layers that are specific to your target task. These layers are typically unfrozen and will be trained using your task-specific data.
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Training: Train the modified model on your task-specific dataset. During training, the weights of the added task-specific layers are adjusted, while the weights of the pretrained layers remain fixed.
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Fine-Tuning: Depending on the performance on your task, you might choose to fine-tune the pretrained layers by unfreezing them. This allows the model to adjust its knowledge to better suit the specifics of your task.
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Evaluation: Evaluate the performance of your transfer learning model on a separate test dataset. This step helps you assess how well the model has adapted to the new task.