NCA-GENM Question 152
Select 3A team is optimizing a multimodal AI model for energy efficiency and accuracy. They decide to use transfer learning and hyperparameter tuning as part of their strategy. Which of the following steps align with best practices for improving performance in this context?
- A
Fine-tune a pre-trained model on a smaller, domain-specific dataset to reduce training time and energy usage.
- B
Use default hyperparameter settings for computational simplicity and faster deployment.
- C
Optimize the learning rate and batch size to improve model convergence and accuracy.
- D
Perform rigorous testing of the model outputs on both individual modalities and combined multimodal data.
- E
Focus solely on increasing the number of training epochs to improve model accuracy.
Show answer and explanation
Correct answers: A, C, D
Explanation
Optimizing multimodal AI models for energy efficiency, trustworthiness, and accuracy requires a combination of transfer learning, hyperparameter tuning, and thorough testing. Fine-tuning pre-trained models, optimizing training settings, and performing robust evaluations are all best practices for achieving these goals. Default settings and simplistic approaches like increasing epochs often fail to address the specific needs of the model, leading to inefficiencies.
- A. Correct.
Fine-tuning a pre-trained model is a key transfer learning technique that leverages pre-existing knowledge, reducing computational demands and improving efficiency.
- B. Incorrect.
Using default hyperparameter settings is not a best practice, as it often leads to suboptimal model performance and missed opportunities for optimization.
- C. Correct.
Optimizing hyperparameters such as learning rate and batch size is essential for improving both the efficiency and accuracy of the model during training.
- D. Correct.
Rigorous testing ensures the model is evaluated for accuracy and trustworthiness across all modalities, which is crucial for multimodal AI systems.
- E. Incorrect.
Solely increasing the number of epochs can lead to overfitting and does not guarantee improved performance, making it a suboptimal approach.