Transfer Learning in ML and AI
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
Transfer learning is a machine learning (ML) technique that reuses a pre-trained model to improve performance on a related task. It's based on the idea that knowledge gained from one task can be applied to another, similar task.
Here are some examples of transfer learning:
- Image classification: A model that can identify dogs can be trained to identify cats by using a smaller image set that highlights the differences between the two animals.
- Computer vision: A model that can identify sharp edges in images can be applied to a smaller set of images to determine the edges in those images.
- Natural language processing: A general language course can be adapted to a specific writing style.
Transfer learning can help reduce the time and computational resources needed to train a new model, and it can lead to significantly higher performance than training from scratch.
However, there are some potential disadvantages to transfer learning, including:
- Domain mismatches: If the two tasks are very different, the pre-trained model might not be the best fit.
- Overfitting: If the model is fine-tuned too much on the second task, it might learn features that don't apply to new data.
- Computational complexity: The pre-trained model and fine-tuning process can be computationally expensive and require specialized hardware.
[More to come ...]
