NCA-GENM Question 163
Select 4A data scientist is training a multimodal generative AI model for text-to-image generation. The model's performance is not meeting the desired quality, and they suspect the issue lies in the model's hyperparameters. Which of the following actions could help improve the model's performance?
- A
Adjust the learning rate to find a better balance between convergence speed and stability.
- B
Increase the batch size to allow the model to learn from more data samples simultaneously.
- C
Reduce the number of training epochs to prevent overfitting during training.
- D
Use a hyperparameter optimization technique such as grid search or Bayesian optimization.
- E
Fine-tune the pre-trained model's weights on a smaller, domain-specific dataset.
Show answer and explanation
Correct answers: A, B, D, E
Explanation
Optimizing the performance of a generative AI model often involves carefully tuning hyperparameters such as learning rate, batch size, and leveraging techniques like hyperparameter optimization and fine-tuning. These adjustments ensure the model converges effectively and adapts to the specific requirements of the task. However, reducing the number of epochs is unlikely to help in this scenario as it may prevent the model from fully learning from the data.
- A. Correct.
Adjusting the learning rate is a crucial step in optimizing model performance. A learning rate that is too high or too low can hinder convergence or lead to unstable training.
- B. Correct.
Increasing the batch size can improve gradient estimation, leading to more stable updates to the model parameters. However, it requires careful consideration of memory constraints.
- C. Incorrect.
Reducing the number of epochs is not typically a strategy for improving performance when the model underperforms, as it may lead to underfitting instead of improving quality.
- D. Correct.
Using hyperparameter optimization techniques like grid search or Bayesian optimization can systematically explore the hyperparameter space, leading to better combinations for the model.
- E. Correct.
Fine-tuning a pre-trained model with domain-specific data can help the model specialize and perform better in the target task by leveraging transfer learning.