NCA-GENM Question 6
Select 3During the training of a multimodal model, you observe instability in loss convergence, with periodic spikes in the loss value. Which of the following actions would most likely help stabilize training in this multimodal setting?
- A
Use gradient clipping to prevent exploding gradients.
- B
Ensure the modalities are properly normalized before inputting into the model.
- C
Increase the learning rate to speed up convergence.
- D
Adjust the weighting of the loss functions for different modalities.
- E
Add more data augmentation for one of the modalities.
Show answer and explanation
Correct answers: A, B, D
Explanation
Training instability in multimodal settings often arises from issues like exploding gradients, imbalanced contributions from modalities, or inconsistent input scales. Techniques such as gradient clipping, input normalization, and loss weighting adjustments directly address these problems and help stabilize the training process. Increasing the learning rate or excessive reliance on data augmentation are less effective in dealing with loss convergence issues.
- A. Correct.
Gradient clipping is a common technique to prevent exploding gradients, which can lead to instability during training. This directly addresses the issue of periodic spikes in the loss value.
- B. Correct.
Proper normalization of input data ensures that the scale of different modalities is consistent, which is crucial for stable training in multimodal setups.
- C. Incorrect.
Increasing the learning rate can exacerbate instability by making the optimization process more aggressive, leading to larger fluctuations in the loss value.
- D. Correct.
Adjusting the weighting of loss functions for different modalities can help balance the contributions of each modality, addressing potential imbalances that could cause instability.
- E. Incorrect.
While data augmentation can improve generalization, it does not directly address training instability caused by loss spikes.