Question 10
Which of the following strategies can help mitigate the risk of overfitting when fine-tuning pretrained models on a small dataset?
Increasing the depth and width of the neural network architecture to capture more complex patterns in the data.
Applying dropout regularization to randomly deactivate neurons during training to prevent co-adaptation of features.
Decreasing the learning rate during fine-tuning to ensure more stable convergence and smoother optimization.
Increasing the batch size during fine-tuning to improve the stability of gradient estimation and accelerate convergence.