NCA-GENM Question 32
Select 3A team is tasked with fine-tuning a multimodal model for a specific use case involving image and text data. They plan to use transfer learning to adapt the model to their domain. Which of the following steps are critical to developing content for multimodal-specific transfer learning?
- A
Curate a domain-specific dataset that includes both image and text pairs relevant to the target task.
- B
Freeze all layers of the pre-trained multimodal model to preserve the original weights during fine-tuning.
- C
Perform data augmentation on both image and text data to increase the diversity of the training dataset.
- D
Ensure that the loss function is designed to handle both image and text modalities effectively.
- E
Replace the pre-trained model's multimodal architecture entirely with a custom-built architecture.
Show answer and explanation
Correct answers: A, C, D
Explanation
Multimodal-specific transfer learning involves adapting a pre-trained multimodal model to a specific domain by curating relevant datasets, enhancing data diversity through augmentation, and ensuring the training process accounts for both modalities effectively. Freezing all layers or discarding the pre-trained architecture would go against the purpose of transfer learning, which is to build upon the knowledge encoded in the pre-trained model.
- A. Correct.
Curating a domain-specific dataset with both image and text pairs is essential for multimodal transfer learning, as it ensures the model learns from data that aligns with the target domain.
- B. Incorrect.
Freezing all layers would prevent the model from adapting to the new domain, which is counterproductive when fine-tuning for a specific use case.
- C. Correct.
Performing data augmentation on both image and text data increases the diversity of the dataset, which can improve the model's robustness and generalization.
- D. Correct.
Designing a loss function that accounts for both modalities ensures the model can learn effectively from both types of data during training.
- E. Incorrect.
Replacing the model's architecture entirely is unnecessary and counter to the principles of transfer learning, which leverage the strengths of pre-trained models rather than starting from scratch.