NCA-GENM Question 31
Select 3You are tasked with fine-tuning a pretrained multimodal model for a healthcare application that combines medical images and textual patient reports. To effectively implement transfer learning, which of the following steps should you prioritize?
- A
Freeze the weights of the pretrained layers and train only the task-specific layers.
- B
Pretrain the model from scratch on the healthcare dataset to ensure domain relevance.
- C
Perform domain-specific pretraining on the medical data to adapt the model to the healthcare domain.
- D
Use a smaller learning rate for the pretrained layers and a larger learning rate for task-specific layers during fine-tuning.
- E
Remove all pretrained embeddings and replace them with randomly initialized embeddings.
Show answer and explanation
Correct answers: A, C, D
Explanation
Transfer learning for multimodal models involves leveraging pretrained weights to save computational resources and improve performance. Freezing layers or using separate learning rates for pretrained and task-specific layers ensures efficient adaptation. Domain-specific pretraining further enhances the model’s ability to handle data from specialized fields like healthcare.
- A. Correct.
Freezing the pretrained layers and training only the task-specific layers is a common transfer learning strategy, especially when the dataset is small or domain-specific.
- B. Incorrect.
Pretraining the model from scratch is time-consuming and computationally expensive, and it negates the benefits of transfer learning from the pretrained multimodal model.
- C. Correct.
Domain-specific pretraining, such as using medical images and text, helps adapt the pretrained multimodal model to the target domain, improving its performance.
- D. Correct.
Using different learning rates for pretrained and task-specific layers allows for controlled fine-tuning, where pretrained layers retain their learned knowledge while task-specific layers are optimized for the new task.
- E. Incorrect.
Replacing pretrained embeddings with randomly initialized embeddings eliminates the advantages of transfer learning and makes the training process less efficient.