NCA-GENM Question 22
Select 2You are working on training a deep generative neural network for multimodal data processing, but you notice that the model is struggling with vanishing gradients in deeper layers, resulting in poor performance. Which of the following architectural solutions can help mitigate this issue by ensuring better gradient flow during backpropagation?
- A
Incorporating residual connections into the network architecture
- B
Using nonsequential layers to allow skipping certain computations
- C
Adding dropout layers to reduce overfitting during training
- D
Using batch normalization to stabilize training
- E
Replacing deeper layers with shallow layers to simplify the model
Show answer and explanation
Correct answers: A, B
Explanation
Vanishing gradients are a common challenge in training deep neural networks, especially for generative AI models that require processing multimodal data. Residual connections and nonsequential architectures directly address this issue by allowing gradients to flow more effectively during backpropagation. These techniques ensure that deeper layers can still contribute to learning without becoming bottlenecks.
- A. Correct.
Residual connections, or skip connections, allow information to bypass certain layers, which helps mitigate the vanishing gradient problem by ensuring gradients can flow more easily through the network.
- B. Correct.
Nonsequential layers, such as those that allow skipping computations, improve gradient flow and avoid bottlenecks in deeper architectures, helping address vanishing gradients.
- C. Incorrect.
While dropout is useful for reducing overfitting, it does not directly address the issue of vanishing gradients, which is related to how gradients are propagated through the network during backpropagation.
- D. Incorrect.
Batch normalization helps stabilize training and speeds up convergence, but it does not directly address the vanishing gradient problem caused by increasing depth in a neural network.
- E. Incorrect.
Replacing deeper layers with shallow layers may simplify the model, but this solution sacrifices the depth required for learning complex representations and does not solve the vanishing gradient issue.