NCA-GENM Question 164
Select 3You are tasked with fine-tuning a multimodal generative AI model using transfer learning. The model combines text and image modalities, and your goal is to enhance its ability to generate accurate captions for images. Which steps are critical for ensuring effective transfer learning in this multimodal setup?
- A
Pre-train the model on a large dataset containing both text and image pairs before fine-tuning.
- B
Ensure the text and image inputs are normalized and tokenized appropriately for their respective encoders.
- C
Fine-tune only the text encoder while freezing the image encoder to save computational resources.
- D
Use a domain-specific dataset during fine-tuning to align the model with the target application.
- E
Ignore cross-attention layers between modalities during fine-tuning as they are already pre-trained.
Show answer and explanation
Correct answers: A, B, D
Explanation
Effective transfer learning in a multimodal setup requires careful consideration of pre-training, data preprocessing, and fine-tuning. Pre-training on a large dataset with paired modalities helps establish a robust foundation. Proper preprocessing ensures data compatibility with the model's architecture. Using a domain-specific dataset during fine-tuning enables the model to specialize in the target application. Avoiding common pitfalls, such as freezing critical components or neglecting cross-modal interactions, is vital for achieving optimal results.
- A. Correct.
Pre-training the model on a large dataset with paired data ensures that the model learns a strong foundation for both modalities before fine-tuning.
- B. Correct.
Proper normalization and tokenization of both text and image inputs are essential to ensure compatibility with the model's encoders and avoid introducing noise.
- C. Incorrect.
Fine-tuning only the text encoder while freezing the image encoder may limit the model's ability to adapt to new data, especially if the image data is domain-specific.
- D. Correct.
Using a domain-specific dataset during fine-tuning aligns the model with the target task or application, which improves performance on real-world use cases.
- E. Incorrect.
Ignoring cross-attention layers during fine-tuning can hinder the model's ability to leverage interactions between text and image modalities, which is crucial for multimodal learning.