Fine-tuning is a fundamental technique in deep learning that enables pre-trained models to adapt to specific tasks.

Introduction to Fine-Tuning

Fine-tuning is the process of taking a pre-trained model and training it further on a new dataset to adapt it to a specific task. Rather than training a deep learning model from scratch, fine-tuning leverages pre-learned knowledge and tailors it for a target domain.

Why is Fine-Tuning Important?

1. Reduces Compute Cost – Training from scratch demands enormous datasets and expensive compute resources. Fine-tuning is cheaper and faster.
2. Leverages Pre-trained Knowledge – Models trained on massive datasets capture generalized features that can be repurposed.
3. Improves Model Performance – Fine-tuning enables domain adaptation, enhancing accuracy in specialized applications.
4. Works on Limited Data – Unlike training from scratch, fine-tuning performs well with smaller datasets.

When Should You Fine-Tune?

• When you lack a large dataset but need a model for a specific task.
• When you have compute constraints and cannot train from scratch.
• When you need to improve performance on a niche dataset.

Fine-Tuning Strategies

Fine-tuning involves selectively updating model weights instead of retraining everything from scratch. The choice of strategy depends on dataset size, compute availability, and task complexity.

Layer Freezing

Gradual Unfreezing

Selective Fine-Tuning