Transfer Learning,

which involves leveraging knowledge gained from one task to improve learning and performance on a different, but related task. Instead of starting the learning process from scratch for each new task or dataset, transfer learning allows models to benefit from previously learned features or representations, thus requiring less data and computation for training. This technique is particularly useful in scenarios where labeled data is limited or expensive to acquire, enabling faster and more efficient model development and deployment across various domains.

Pointers

Feature Reuse

Transfer learning allows you to reuse pre-trained models’ feature layers to solve new tasks.

Fine-tuning

You can fine-tune only the last few layers of a pre-trained model for faster and efficient training.

Smaller Data

Transfer learning enables you to work with smaller datasets by leveraging knowledge from large models.

Faster Training

Since the model is pre-trained, transfer learning significantly reduces the training time for new tasks.

Domain Shift

Ensure that the pre-trained model's domain aligns with your task to avoid a negative transfer.

Layer Freezing

Freezing early layers helps retain the original model's learned features while adjusting to new data.

Task Specific

Transfer learning works best when tasks are similar, as it allows for a smoother knowledge transfer.

Avoid Overfitting

By using a pre-trained model, you reduce the chances of overfitting when dealing with smaller datasets.

Initial Weights

Transfer learning provides excellent initial weights for your model, often outperforming random initialization.