Transfer learning is a technique in AI where a pre-trained model is adapted for a different but related

task. Here’s a detailed explanation:

Transfer Learning: This involves taking a base model that has been pre-trained on a large dataset and

fine-tuning it on a smaller, task-specific dataset.

Base Weights: The existing base weights from the pre-trained model are reused and adjusted slightly

to fit the new task, which makes the process more efficient than training a model from scratch.

Benefits: This approach leverages the knowledge the model has already acquired, reducing the

amount of data and computational resources needed for training on the new task.

Reference:

Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A Survey on Deep Transfer Learning. In

International Conference on Artificial Neural Networks.

Howard, J., & Ruder, S. (2018). Universal Language Model Fine-tuning for Text Classification. In

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:

Long Papers).