During training, a deep learning model's validation loss stops improving after epoch 12 while training loss continues to fall. Which single adjustment is most likely to correct this behaviour?
- AIncrease the learning rate to allow the optimiser to escape the current minimum faster.
- BSwitch the activation function from ReLU to sigmoid in all hidden layers.
- CAdd dropout layers or increase the dropout probability to regularise the network. Correct
- DDouble the batch size so each gradient update is computed from more samples.
Why A is wrong: Tempting because a low learning rate can cause slow convergence, but raising it here would worsen overfitting by letting the model fit training noise even more aggressively, widening the gap between training and validation loss.
Why B is wrong: Tempting because activation functions affect gradient flow, but sigmoid is prone to vanishing gradients in deep networks and does not address overfitting. Replacing ReLU with sigmoid would likely degrade convergence without reducing the training-validation gap.
Why C is correct: Dropout randomly deactivates units during training, preventing co-adaptation and reducing overfitting. When validation loss plateaus while training loss keeps falling, the model is memorising training data, and regularisation via dropout directly addresses that cause.
Why D is wrong: Tempting because larger batches can smooth gradient estimates, but doubling batch size without other changes often sharpens minima and can worsen generalisation. It does not introduce the regularisation effect needed to close the training-validation loss gap.