Early stopping is a classic regularization technique for deep neural networks that mitigates overfitting by constraining the number of training epochs instead of directly penalizing weight values. This approach is motivated by the fact that neural networks tend to fit clean data before memorizing noisy labels; by halting training at the optimal epoch, the model avoids interpolating noise and thereby improves generalization.

Early Stopping in Deep Learning

Deep neural networks trained on data with mislabeled examples demonstrate a distinct learning trajectory: they initially fit the cleanly labeled examples and only later begin to interpolate the mislabeled ones. By terminating the training process after the clean data is learned but prior to the memorization of random labels, practitioners can effectively guarantee better generalization.

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In machine learning, interpolation of training data occurs when an over-parameterized model perfectly fits the training dataset, achieving zero training error. Because large neural networks possess vastly more parameters than necessary to fit the data, they have the capacity to memorize every training example. This tendency to interpolate is a key mechanism that allows deep neural networks to behave more like nonparametric models.

Interpolation of Training Data

Label noise occurs when a training dataset contains incorrectly or randomly assigned labels, introducing intrinsic variability into the learning process. For example, when predicting mortality among patients, the true outcome may be ambiguous or recorded incorrectly. While neural networks can eventually interpolate these noisy labels, their presence makes regularization techniques like early stopping crucial to prevent overfitting.

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