NCA-GENM Question 29
Select 3You are tasked with fine-tuning a multimodal model for a content recommendation system that uses both text and image data. To improve the performance of the model, you decide to use transfer learning. Which of the following steps are critical for applying multimodal-specific transfer learning effectively?
- A
Pretrain the model on a large dataset containing both text and image modalities relevant to the target domain.
- B
Freeze all layers of the pretrained model and only train the final classification layer on the new dataset.
- C
Align the feature representations of text and image data during the fine-tuning process.
- D
Ensure that the image and text datasets are of equal size before initiating transfer learning.
- E
Leverage domain-specific data augmentation techniques for both text and image inputs during fine-tuning.
Show answer and explanation
Correct answers: A, C, E
Explanation
Effective multimodal-specific transfer learning requires leveraging a pretrained model that understands the relationships between text and image modalities, ensuring alignment between their feature representations, and using domain-specific data augmentation techniques to improve generalization. Freezing all layers and ensuring dataset size equality are either limiting or unnecessary for this purpose.
- A. Correct.
Pretraining on a dataset that includes both modalities (text and image) ensures that the model has a foundational understanding of their relationships, which is crucial for transfer learning in multimodal tasks.
- B. Incorrect.
Freezing all layers of the pretrained model limits the model's ability to adapt to the new dataset and new domain-specific features, which is not ideal for multimodal transfer learning.
- C. Correct.
Aligning feature representations of text and image data ensures that the model can effectively integrate information from both modalities, which is critical for improving performance.
- D. Incorrect.
Ensuring equal dataset size is not necessary for multimodal transfer learning. Techniques like oversampling or balanced loss functions can handle imbalances without requiring exact equality.
- E. Correct.
Domain-specific data augmentation helps the model generalize better to the target domain by diversifying the examples it sees during fine-tuning.