Both Transfer Learning and Fine-Tuning are techniques to improve deep learning models by using a pre-trained model rather than training from scratch.
1️⃣ What is Transfer Learning?
✅ Use a pre-trained model (like ResNet, VGG, BERT) on a new task.
✅ Keep most layers frozen (do not update weights).
✅ Only retrain the last few layers (usually the classification head).
✅ Faster training, useful for small datasets.
🔹 Example:
Using a model trained on ImageNet to classify medical images by replacing the last layer.
✅ Best for: When you have small datasets and want to leverage powerful pre-trained features.
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2️⃣ What is Fine-Tuning?
✅ Same as transfer learning, but unfreeze some pre-trained layers and train them on new data.
✅ Allows the model to adapt better to the new dataset.
✅ Works best when new dataset is large and somewhat similar to the original dataset.
✅ Takes more time, but higher accuracy than basic transfer learning.
🔹 Example:
Fine-tuning the last 4 layers of VGG16 to improve accuracy on a new dataset.
3️⃣ Transfer Learning vs. Fine-Tuning: Key Differences
4️⃣ Which One Should You Use? 🤔
✔ Small dataset? → Use Transfer Learning (Freeze all but last layers).
✔ Large dataset? → Use Fine-Tuning (Unfreeze and retrain deeper layers).
✔ Need maximum accuracy? → Start with Transfer Learning, then Fine-Tune.
5️⃣ When to Avoid Fine-Tuning?
❌ If you don’t have enough data, fine-tuning can cause overfitting.
❌ If the pre-trained model’s features don’t match your dataset, it may not help much.
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