Fine tuning is a machine-learning technique that involves making small adjustments to a pre-trained model to improve its performance on a specific task. This is more efficient and often yields better results than training a model from scratch, as the model already has a good understanding of the world and can leverage this knowledge to learn the new task more quickly.
Why is Fine Tuning is important?
Data Efficiency
Fine-tuning allows for effective model adaptation with limited task-specific data. Instead of collecting and annotating a new dataset, you can use existing, pre-trained models and optimize them for your task. This saves time and resources.
Transfer of Knowledge
Pre-trained models have already learned valuable features and patterns from vast datasets during their initial training. Fine-tuning allows this acquired knowledge to be transferred to your specific task. It enables your model to start from a point of enhanced understanding.
Specialization
Fine-tuning lets you customize a model to excel at a specific task. It’s like tailoring a versatile suit to fit an individual. By adjusting the model’s settings, you create a tool that works great in specific situations.
Time Efficiency
Creating a model from scratch takes a long time, especially for deep and complex designs. Fine-tuning speeds up the process, as the model begins with already learned features. It reduces the time needed for convergence.
Resource Saving
Training deep learning models demands considerable computational resources. Fine-tuning is resource-efficient as it leverages existing models and necessitates less extensive training.
How does Fine Tuning work?
To fine-tune a model, first choose a pre-trained model that has been trained on a large and diverse dataset. This model will be used as a foundation with the acquired characteristics and representations. Next, prepare your task-specific dataset. This dataset should be relevant to your target task and ideally include labelled examples for supervised tasks. Organize the dataset to align with the input format the pre-trained model expects.
Depending on the nature of your task, you might need to modify the architecture of the pre-trained model. This could involve adjusting the number of output units in the final layer for classification tasks or modifying layers to suit specific input types.