Transfer Learning and Fine-Tuning Pre-trained Models

Definition: Technique where a model trained on one task is reused as a starting point for a model on a different but related task.
Advantages:
- Speeds up training process by leveraging knowledge from pre-trained models.
- Requires less labeled data for new tasks.
- Helps generalize to new datasets or domains.
Process:
- Select Pre-trained Model: Choose a model pre-trained on a large dataset (e.g., ImageNet).
- Adaptation: Replace the final layer(s) of the pre-trained model with new layers suited for the target task.
- Fine-Tuning: Optionally, fine-tune the entire or part of the pre-trained model on new data to improve performance.

Definition: Adjusting the parameters of a pre-trained model on a new dataset to improve performance on a specific task.
Steps:
- Freezing Layers: Initially, freeze most of the pre-trained model’s layers to retain learned features.
- Training: Train the model on the new dataset while adjusting the weights of the unfrozen layers.
- Gradual Unfreezing: Optionally, unfreeze and fine-tune more layers as needed, typically starting from the end layers.
Use Cases:
- Image Classification: Enhance accuracy on specific classes or domains not well-represented in the original pre-training dataset.
- Object Detection: Improve detection accuracy by fine-tuning on new object classes or environments.
- Semantic Segmentation: Adapt pre-trained models to accurately segment images in new contexts like satellite or medical imaging.

Data Similarity: Ensure the new dataset is sufficiently similar to the pre-training dataset for effective transfer.
Overfitting: Monitor for overfitting when fine-tuning, adjust regularization techniques as needed.
Computational Resources: Fine-tuning can be resource-intensive, especially for deep models; balance model complexity with available resources.