Computer Science – 18.1 Artificial Intelligence (AI) | e-Consult

Transfer Learning: Transfer learning is a machine learning technique where a model trained on one task is re-purposed on a second, related task. Instead of training a model from scratch on the new task, the knowledge gained from the previous task is transferred to accelerate and improve learning on the new task. This typically involves using the pre-trained model as a starting point and fine-tuning it on the new dataset.

Beneficial Scenario: Transfer learning is particularly beneficial when there is a limited amount of labeled data available for the new task. Example: Image classification. Suppose you want to build a model to classify different types of flowers, but you only have a small dataset of flower images. Instead of training a model from scratch, you can use a pre-trained model (e.g., ImageNet) that has been trained on a massive dataset of general images. The pre-trained model has already learned to extract useful features from images (e.g., edges, shapes, textures). You can then fine-tune the model on your flower dataset, which will allow it to quickly learn to identify the different types of flowers. This approach significantly reduces the amount of training data required and improves the model's performance.

Why it's beneficial:

• Reduced Training Time: Fine-tuning a pre-trained model is much faster than training a model from scratch.
• Improved Performance: Transfer learning often leads to better performance, especially when the new task has limited data.
• Lower Data Requirements: It reduces the need for large labeled datasets for the new task.