NCA-GENM Question 30
Select 3You are tasked with fine-tuning a multimodal generative AI model for a project requiring both image and text generation. Which of the following are necessary steps to develop content for multimodal-specific transfer learning?
- A
Preprocess and align multimodal datasets to ensure data consistency across modalities.
- B
Freeze all layers of the pretrained model to prevent weight updates during fine-tuning.
- C
Incorporate modality-specific embeddings to enhance the representation of each data type.
- D
Train the model only on text data while ignoring the image modality to simplify the process.
- E
Evaluate the model using benchmark datasets that include both text and image components.
Show answer and explanation
Correct answers: A, C, E
Explanation
To develop content for multimodal-specific transfer learning, it's important to preprocess and align datasets, use modality-specific embeddings to enhance the model's representation capabilities, and evaluate the model using appropriate benchmarks. These steps ensure that the model can effectively learn and generate outputs across multiple modalities, which is the core purpose of multimodal generative AI systems.
- A. Correct.
Preprocessing and aligning multimodal datasets are crucial to ensure that the image and text data are properly paired and consistent, which is essential for successful transfer learning.
- B. Incorrect.
Freezing all layers of the pretrained model would prevent the model from adapting to the new multimodal task, which is not suitable for fine-tuning.
- C. Correct.
Incorporating modality-specific embeddings is a key step to improve the representation and understanding of each modality during the training process.
- D. Incorrect.
Training the model only on text data while ignoring the image modality would fail to leverage the multimodal capabilities of the model, defeating the purpose of multimodal transfer learning.
- E. Correct.
Evaluating the model using benchmark datasets ensures that the model's performance is assessed on both text and image outputs, which is critical for multimodal applications.