Transfer Learning

A machine learning method where the application of knowledge obtained from a model used in one task, can be reused as a foundation point for another task. It's typically used when labeled data for the target task is limited, however a pretrained model exist or can be trained on related tasks.


Process:

  1. Pre-Trained Model: Selection or creation of a pretrained model that has been trained on a large dataset for a related task.
  2. Feature Extraction: Extract relevant features from the pretrained model, which capture general patterns and information from the original task. It's usually done by replacing the last few layers on the base model with new layers.
  3. Training and Fine-tuning: Adjust and update the parameters of the pretrained model using the limited labeled data from the target task, allowing it to adapt to the specific task.

Notes:

  • Transfer learning can significantly reduce the need for large labeled datasets, especially in domains where data is scarce.
  • It allows for the efficient reuse of knowledge and representations learned from one task to benefit another.
  • The choice of the pretrained model and the similarity between the original and target tasks are critical for successful transfer learning.
  • Fine-tuning the pretrained model requires careful consideration of hyperparameters and regularization to prevent overfitting.