Early stopping is a technique used in deep learning to prevent overfitting by halting the training process before the model becomes too complex for the given dataset. Overfitting occurs when a model learns the training data too well, capturing noise and details that aren’t representative of new, unseen data. By monitoring the model’s performance on a validation set during training, early stopping decides the optimal moment to stop training, ensuring the model retains its generalization ability.
During the training of a neural network, the model's performance is typically measured by its loss on both training and validation datasets. Initially, as training progresses, both losses decrease. However, after a certain point, the training loss may continue to decrease, while the validation loss starts to increase, indicating that the model is beginning to overfit. Early stopping keeps an eye on these losses and defines a strategy to stop training once the validation loss does not improve for a set number of epochs. For example, if you set a patience value of 10 epochs, the training will stop if the validation loss does not improve after 10 consecutive updates.
This technique not only helps in achieving a better model performance on new data but also saves computation time by preventing unnecessary training cycles. In practical terms, consider a scenario where you are training a model for image classification. If you notice that, after a certain number of epochs, your training accuracy continues to rise while your validation accuracy plateaus or drops slightly, implementing early stopping would allow you to save that last "best" version of the model, which is less likely to make mistakes on unseen images, thus enhancing its accuracy and reliability in real-world applications.