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Early Stopping
A regularization technique that halts training when validation performance stops improving, preventing overfitting.
Early Stopping is a regularization technique that halts training when the model's performance on a validation set stops improving, even if the training loss continues to decrease. The rationale is that after a certain point, continued training causes the model to fit noise and idiosyncrasies in the training data rather than learning generalizable patterns — i.e., overfitting. Early stopping prevents this by preserving the model at the point of best validation performance.
The standard implementation monitors a validation metric (e.g., validation loss or accuracy) after each epoch. If the metric does not improve for a specified number of consecutive epochs (the 'patience' parameter), training is stopped. The model weights from the epoch with the best validation metric are typically saved and used for deployment. Some implementations use a more nuanced criterion, such as requiring the validation loss to decrease by at least a minimum threshold (min_delta) to count as an improvement.
Early stopping is computationally efficient — it avoids wasting compute on epochs that would only overfit — and acts as implicit regularization by limiting the effective number of training steps. It is one of the simplest and most effective regularization techniques, often used in combination with other methods like weight decay and dropout. The patience parameter trades off between training thoroughness (higher patience allows more epochs, potentially finding better solutions) and overfitting risk (lower patience stops sooner).
- ■Halts training when validation performance stops improving
- ■Patience parameter: number of epochs to wait before stopping
- ■Saves model weights from the epoch with best validation metric
- ■Computationally efficient; acts as implicit regularization
- ■Often combined with weight decay and dropout
Monitoring validation loss during fine-tuning with patience=3, stopping training if the loss doesn't improve for 3 consecutive epochs and saving the best checkpoint.