Which technique is commonly used to prevent overfitting during training?

Dropout regularization is a technique specifically designed to prevent overfitting during the training of neural networks. Overfitting occurs when a model learns to capture noise and random fluctuations in the training data rather than the underlying distribution. To mitigate this, dropout randomly "drops out" a fraction of the neurons during training, meaning that they are temporarily removed from the network for each training iteration. This forces the model to learn more robust features that are useful in generalizing to unseen data, rather than relying on specific neurons that may only perform well on the training set.

By introducing this randomness, dropout helps ensure that neurons do not co-adapt too heavily to the training data, thus promoting the development of more robust models that generalize better to new data. This technique has been widely adopted because it is easy to implement and has proven effective in various neural network architectures.

The other options, while relevant to training models, do not directly address overfitting in the same way that dropout does. For instance, increasing model complexity might actually exacerbate overfitting. Normalizing input data is essential for training stability and efficiency but does not directly control overfitting. Reducing the learning rate can help with convergence issues but may not necessarily protect against overfitting