What is a major advantage of transfer learning in Generative AI?

What is a major advantage of transfer learning in Generative AI?

• A. It slows down training times significantly
• B. It allows for complete retraining of models from scratch
• C. It enables models to use knowledge from pre-trained sources
• D. It relies exclusively on new data acquisition

Answer: (C)

The choice that highlights a major advantage of transfer learning in Generative AI is that it enables models to use knowledge from pre-trained sources. Transfer learning involves taking a model that has already been trained on a large dataset and fine-tuning it for a specific task or domain with a smaller, related dataset. This process leverages the knowledge captured during the initial training phase, allowing the model to achieve better performance with significantly less data and computational resources compared to training a new model from scratch. By utilizing existing learned features, the model can quickly adapt to new tasks while retaining relevant information, which is particularly beneficial in scenarios where data may be scarce or difficult to collect. This not only enhances the efficiency of the development process but also improves the overall performance of the model in the target application.

The choice that highlights a major advantage of transfer learning in Generative AI is that it enables models to use knowledge from pre-trained sources. Transfer learning involves taking a model that has already been trained on a large dataset and fine-tuning it for a specific task or domain with a smaller, related dataset. This process leverages the knowledge captured during the initial training phase, allowing the model to achieve better performance with significantly less data and computational resources compared to training a new model from scratch.

By utilizing existing learned features, the model can quickly adapt to new tasks while retaining relevant information, which is particularly beneficial in scenarios where data may be scarce or difficult to collect. This not only enhances the efficiency of the development process but also improves the overall performance of the model in the target application.