NCA-GENM Question 159
Select 3You are training a generative AI multimodal model and notice that the model's validation loss plateaus while the training loss continues to decrease. Which of the following strategies could help optimize the model's performance?
- A
Use a learning rate scheduler to adjust the learning rate dynamically during training.
- B
Increase the size of the training dataset by incorporating more diverse examples.
- C
Reduce the batch size to improve gradient updates and prevent overfitting.
- D
Apply early stopping to halt training when validation loss stops improving.
- E
Increase the model's capacity by adding more layers or parameters.
Show answer and explanation
Correct answers: A, B, D
Explanation
The plateau in validation loss while training loss decreases suggests overfitting or suboptimal learning. Strategies such as using a learning rate scheduler, increasing dataset diversity, or applying early stopping can address these issues. Increasing model capacity or reducing batch size may not directly resolve the problem and could potentially worsen it.
- A. Correct.
Using a learning rate scheduler can help optimize the training process by preventing overtraining or undertraining, which might be causing the plateau in validation loss. This is a valid approach.
- B. Correct.
Increasing the size and diversity of the training dataset helps the model generalize better, which can reduce the gap between training and validation loss. This is a valid approach.
- C. Incorrect.
Reducing the batch size generally does not address the issue of overfitting, especially when the training loss is still decreasing. This is not a valid approach in this scenario.
- D. Correct.
Early stopping is a common technique to prevent overfitting when validation loss stops improving, ensuring the model does not overtrain. This is a valid approach.
- E. Incorrect.
Increasing the model's capacity could exacerbate overfitting since the validation loss is already plateauing. This is not a recommended approach in this scenario.