NCA-GENM Question 167
Select 3A team is tasked with fine-tuning a multimodal generative AI model for a specific domain, such as medical imaging and text descriptions. During the transfer learning process, which steps are most critical for developing content that ensures the model adapts effectively to the new domain?
- A
Curate a domain-specific dataset that includes both textual and visual data relevant to the target domain.
- B
Use only pre-trained weights from the visual encoder and freeze the text encoder during fine-tuning.
- C
Ensure data augmentation techniques are applied to both text and image data to increase diversity.
- D
Train the model from scratch without leveraging pre-trained weights from the base multimodal model.
- E
Evaluate the model's performance using domain-specific metrics to ensure relevance.
Show answer and explanation
Correct answers: A, C, E
Explanation
When developing content for multimodal-specific transfer learning, it is vital to curate a high-quality, domain-specific dataset that includes both text and image data. Applying data augmentation techniques enhances diversity and improves generalization, while evaluation using domain-specific metrics ensures the model meets the specific requirements of the application. These steps collectively ensure the transfer learning process is effective and the model is well-adapted to the target domain.
- A. Correct.
Curating a domain-specific dataset ensures the model is exposed to relevant multimodal content, which is critical for effective transfer learning in the target domain.
- B. Incorrect.
Freezing the text encoder restricts the model's ability to adapt to domain-specific textual nuances, which can reduce overall performance.
- C. Correct.
Data augmentation increases the diversity of training samples, helping the model generalize better to unseen multimodal data in the target domain.
- D. Incorrect.
Training a model from scratch is resource-intensive and ignores the benefits of leveraging pre-trained weights, which can significantly accelerate adaptation to the new domain.
- E. Correct.
Using domain-specific metrics helps validate whether the model performs well in the intended application, ensuring practical relevance.