NCA-GENM Question 160
Select 4A company is training a multimodal generative AI model that synthesizes text and images. The model is underperforming in terms of accuracy and convergence time. Which of the following actions can help optimize the model's performance?
- A
Adjust the learning rate to find a balance between convergence speed and accuracy
- B
Increase the batch size to improve the stability of gradient updates
- C
Use different weights for loss functions of text and image outputs to balance their contributions
- D
Remove dropout layers to ensure all neurons are actively contributing to training
- E
Reduce the number of parameters in the model by pruning less important connections
Show answer and explanation
Correct answers: A, B, C, E
Explanation
Optimizing AI model performance involves adjusting hyperparameters and architectural choices to improve convergence, accuracy, and efficiency. Techniques like tuning the learning rate, modifying batch size, balancing loss functions, and reducing unnecessary parameters are effective strategies. However, removing dropout layers is generally not recommended because it can lead to overfitting, particularly in complex models like multimodal generative AI.
- A. Correct.
Adjusting the learning rate is a common hyperparameter tuning technique that can significantly impact convergence speed and model accuracy. Too high a learning rate may lead to divergence, while too low can result in slow training.
- B. Correct.
Increasing the batch size can improve the stability of gradient updates by reducing noise in the gradients, which often leads to better convergence.
- C. Correct.
Assigning different weights to the loss functions for text and image outputs can help balance their contributions, especially in multimodal models where one modality might dominate.
- D. Incorrect.
Removing dropout layers can increase overfitting, as dropout is a regularization technique designed to prevent the model from relying too heavily on specific neurons during training.
- E. Correct.
Pruning less important connections reduces the number of parameters in the model, which can improve computational efficiency and reduce overfitting without significant loss of performance.