NCA-GENM Question 165
Select 3You are tasked with fine-tuning a multimodal model for generating product descriptions using both textual and image data. To effectively perform transfer learning in this scenario, which steps should you prioritize to develop the content for multimodal-specific transfer learning?
- A
Preprocess both textual and image data to standardize formats and remove noise.
- B
Train a completely new model from scratch using the multimodal data.
- C
Freeze the pre-trained model's feature extraction layers while fine-tuning task-specific layers.
- D
Ensure that the multimodal embeddings from text and image modalities align during the fine-tuning process.
- E
Exclude the text modality entirely if you are confident about the quality of the image data.
Show answer and explanation
Correct answers: A, C, D
Explanation
To successfully develop content for multimodal-specific transfer learning, you need to preprocess your data, make use of pre-trained model features, and ensure that embeddings from different modalities are effectively aligned. This approach helps leverage the strengths of the pre-trained model while adapting it to your specific use case without unnecessary computational overhead.
- A. Correct.
Preprocessing is critical to ensure that both textual and image data are in a consistent format, which is essential for effective transfer learning in multimodal models.
- B. Incorrect.
Training a new model from scratch is often unnecessary and computationally expensive when a pre-trained multimodal model is available for transfer learning.
- C. Correct.
Freezing pre-trained layers allows you to retain the general features learned during pre-training while focusing computational resources on adapting the model to the specific task.
- D. Correct.
Aligning embeddings ensures that the model understands the relationship between text and image data, which is crucial for effective multimodal transfer learning.
- E. Incorrect.
Excluding a modality (e.g., text) reduces the richness of multimodal learning and undermines the purpose of leveraging both modalities.