Introduction to Fine-Tuning
Fine-tuning is adapting a pre-trained machine learning model to a new, specific task by retraining it on a smaller, task-related dataset. Building a machine learning model from scratch can be expensive. It requires vast amounts of data, computational power, and time. However, fine-tuning offers an efficient solution, making it possible to build a new model with limited resources. Imagine fine-tuning as tailoring a dress. The pre-trained model is like a ready-to-wear dress that fits most people well. Unfortunately, sometimes certain people need adjustments to specific areas, such as the waist or sleeves. So the dress must be fine-tuned to fit perfectly.
Fine-tuning has become especially effective for domain-specific tasks. The pre-trained model already learns patterns from large datasets. One pre-trained model can be trained into several domain-specific models. For example, a Convolutional Neural Network (CNN) model can be fine-tuned to classify medical images, enabling AI systems to detect brain tumors or breast cancer earlier than the human eye. The same model can also fine-tuned to recognize individuals based on their facial images.
How does Fine-Tuning work?
The general process generally including freezing certain layers includes:
The first step of fine-tuning begins by loading the pre-trained model. This pre-trained model would be the foundation of the fine-tuned model after being trained on new datasets. Here's a step-by-step breakdown of how the process works:
Loading the pre-trained model: The pre-trained model is already trained with a general-purpose dataset. They learn patterns and features from the vast amount of data and are ready to fine-tune by domain or task-specified dataset to be a new model.
Freezing certain layers: A pre-trained model has layers with their own weight, the result of previous training. Freezing these layers means their parameters remain unchanged during the fine-tuning process, preserving the general knowledge the model has learned. For example, The lower layers of the CNN model capture generic features like edges or shapes. When training brain tumor classification images, this layer definitely should be frozen.
Illustrated layers:
By en:User:Cburnett - This W3C-unspecified vector image was created with Inkscape ., CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=1496812
- Updating higher layers: The higher layers capture more task-specific patterns. This layer will be retrained on the new dataset.
In the real world layers are more complicated:
By Akritasa - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=41652806
- Avoid overfitting: By freezing the lower layers and retraining only the higher layers, the model avoids overfitting to the small task-specific dataset. This allows the model to retain its ability to generalize to new data.
Practical Fine-tuning
Fine-tuning excels in task-specific models such as text summarization, code generation, classification, etc., While at domain-specific tasks, fine-tuning produces a new model that helps tremendously in their specific domain these are some examples:
- Med-PaLM 2 is a version of the PaLM 2 model fine-tuned to answer medical questions with high accuracy .
- PharmaGPT, fine-tuned from the LLaMA model, specializes in tasks related to biopharmaceuticals and chemical industries.
Challenges and Best Practices in Fine-Tuning
While fine-tuning offers a practical way to use pre-trained models for specific tasks, it comes with its own challenges. Fine-tuning often works with small datasets so it is prone to overfitting. To overcome this ML practitioners should experiment with layers. Selecting layers to fine-tune is a tough challenge, there is no definite answer to which layer to freeze or update. Experimenting with different layers gives us an idea of which layers to freeze or update and find the right balance between generalization and specificity.
Conclusion
Fine-tuning is a powerful technique that enables us to create highly specialized models efficiently. Using the knowledge learned from pre-trained models and tailoring them to specific tasks, we can achieve impressive results in various domains. As machine learning evolves, fine-tuning will remain a key method for leveraging pre-trained models to solve domain-specific problems efficiently.
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