What is Early Stopping?

Early stopping is a form of regularization that halts the training process when the performance of the model on a validation dataset starts to degrade. Instead of training the model until convergence, early stopping monitors the validation error during training and stops the training process when the validation error begins to increase.

Advantages of Early Stopping

• Prevents Overfitting: The primary objective of early stopping is to prevent overfitting by monitoring the model’s performance on a validation dataset during training. By halting the training process when the validation error starts to increase, early stopping prevents the model from becoming excessively complex and memorizing noise in the training data.
• Conserves Computational Resources: Training deep neural networks can be computationally intensive, especially with large datasets and complex architectures. Early stopping helps conserve computational resources by terminating the training process when further improvement in validation performance is unlikely. This leads to reduced training time and computational costs.
• Enhances Generalization: By curbing overfitting, early stopping encourages the model to generalize better to unseen data. Models trained with early stopping demonstrate improved performance on unseen datasets or real-world applications, as they capture underlying patterns without being swayed by noise or irrelevant details.
• Simple Implementation: Unlike some other regularization techniques that require tuning hyperparameters or modifying the model architecture, early stopping is straightforward to implement and requires minimal additional effort. It involves monitoring the validation error during training and halting the process when a predefined criterion, such as no improvement for a certain number of epochs, is met.

Overfitting is a common challenge in training neural networks. It occurs when a model learns to memorize the training data rather than generalize patterns from it, leading to poor performance on unseen data. While various regularization techniques like dropout and weight decay can help combat overfitting, early stopping stands out as a simple yet effective method to prevent neural networks from overfitting. In this article, we will demonstrate how can we reduce overfitting in neural networks.