NCA-GENM Question 168
Select 3A team is fine-tuning a multimodal generative AI model for a specific domain using transfer learning. The model integrates text and image modalities. Which practices should they adopt to ensure effective multimodal-specific transfer learning?
- A
Use domain-specific datasets that contain both text and image pairs.
- B
Freeze all layers of the pre-trained model to retain original weights.
- C
Apply regularization techniques to prevent overfitting during fine-tuning.
- D
Ensure the multimodal alignment between text and image embeddings is preserved.
- E
Train the model from scratch using a large general-domain dataset.
Show answer and explanation
Correct answers: A, C, D
Explanation
Effective multimodal-specific transfer learning requires adapting pre-trained models to a specific domain while retaining their multimodal capabilities. Using domain-specific datasets with text and image pairs enables the model to learn domain-relevant relationships. Regularization techniques help the model avoid overfitting during fine-tuning, while preserving multimodal alignment ensures the embeddings remain meaningful. Freezing all layers or training from scratch are suboptimal strategies for transfer learning.
- A. Correct.
Using domain-specific datasets with text and image pairs ensures the model learns the unique relationships between modalities relevant to the target domain. This is crucial for multimodal-specific transfer learning.
- B. Incorrect.
Freezing all layers of the pre-trained model is not ideal for transfer learning as it prevents the model from adapting to the specific domain. Instead, selective fine-tuning is recommended.
- C. Correct.
Regularization techniques, such as dropout or weight decay, help prevent overfitting, ensuring the model generalizes well to new data within the domain.
- D. Correct.
Maintaining multimodal alignment ensures that the relationships between text and image embeddings remain consistent, which is critical for effective multimodal transfer learning.
- E. Incorrect.
Training from scratch negates the benefits of transfer learning by discarding the pre-trained model's knowledge. This approach is resource-intensive and not suitable for domain-specific fine-tuning.