What is meant by "transfer learning" in AI?

What is meant by "transfer learning" in AI?

• A. Using multiple learning models simultaneously
• B. Utilizing a pre-trained model on related tasks
• C. Creating unique models for each task
• D. Training from scratch on new tasks

Answer: (B)

Transfer learning in AI refers to the practice of taking a model that has been previously trained on a large dataset for one task and adapting it to a related task with less data. This approach leverages the knowledge gained from the initial training to improve performance and efficiency on the new task. By utilizing a pre-trained model, one can often achieve better results than starting from scratch, since the model already understands general features and patterns that can be useful across similar tasks. For example, if a model was trained to recognize objects in images, it can be fine-tuned on a smaller dataset specific to a new class of images, allowing for quicker convergence and often better accuracy compared to training anew. This capability is especially valuable in scenarios where labeled data is scarce or expensive to acquire.

Transfer learning in AI refers to the practice of taking a model that has been previously trained on a large dataset for one task and adapting it to a related task with less data. This approach leverages the knowledge gained from the initial training to improve performance and efficiency on the new task. By utilizing a pre-trained model, one can often achieve better results than starting from scratch, since the model already understands general features and patterns that can be useful across similar tasks.

For example, if a model was trained to recognize objects in images, it can be fine-tuned on a smaller dataset specific to a new class of images, allowing for quicker convergence and often better accuracy compared to training anew. This capability is especially valuable in scenarios where labeled data is scarce or expensive to acquire.