NCA-GENM Question 166
Select 3You are training a multimodal generative AI model to process both text and image data for a product recommendation system. To improve the model's understanding of relationships between modalities, you decide to use transfer learning. Which of the following steps are critical to ensuring successful multimodal-specific transfer learning?
- A
Fine-tune the pretrained model on a multimodal dataset that includes both text and image data.
- B
Ensure that the pretrained model has been trained exclusively on unimodal datasets.
- C
Use a shared encoder-decoder architecture to learn aligned embeddings for text and image modalities.
- D
Freeze all layers of the pretrained model to retain its original weights during training.
- E
Preprocess text and image data to ensure consistency in feature extraction and representation.
Show answer and explanation
Correct answers: A, C, E
Explanation
To develop content for multimodal-specific transfer learning, it is important to fine-tune the pretrained model on a multimodal dataset, use architectures that align embeddings across modalities, and preprocess data for consistent representation. These steps ensure that the model can effectively learn and integrate information from both text and image modalities, enabling successful transfer learning.
- A. Correct.
Fine-tuning on a multimodal dataset is essential for leveraging the pretrained model's knowledge and adapting it to understand relationships between text and image data.
- B. Incorrect.
Using a pretrained model exclusively trained on unimodal datasets limits its ability to understand cross-modal relationships, which is critical for multimodal tasks.
- C. Correct.
A shared encoder-decoder architecture facilitates learning aligned embeddings for text and images, enabling the model to understand and integrate both modalities effectively.
- D. Incorrect.
Freezing all layers of the pretrained model prevents fine-tuning, which is necessary to adapt the model's weights for the specific multimodal task.
- E. Correct.
Preprocessing ensures that text and image data are represented consistently, which is crucial for effective learning in multimodal tasks.