Data augmentation can mitigate the impact of noisy labels by reducing a model’s tendency to memorize incorrect examples and encouraging it to focus on generalizable patterns. Noisy labels—incorrect or mislabeled data points—often lead models to overfit to errors, especially when training data is limited. By generating diverse variations of existing data (e.g., rotating images, adding background noise to audio, or paraphrasing text), augmentation increases the effective size of the dataset. This forces the model to rely on broader features shared across augmented samples rather than memorizing specific artifacts tied to noisy labels. For example, if an image of a dog is mislabeled as a cat, applying rotations, crops, or color shifts creates multiple versions of the image. The model must now reconcile these variations with the same incorrect label, which becomes harder as inconsistencies grow. Over time, the model may downweight such examples due to conflicting signals, reducing their influence.
Another approach involves using augmentation to identify and correct label errors. When models are trained on augmented data, their predictions on transformed samples can reveal inconsistencies. For instance, if a model consistently predicts “dog” for all augmented versions of an image originally labeled “cat,” this discrepancy suggests the label might be incorrect. Developers can flag such examples for manual review or automated correction. Techniques like “test-time augmentation” extend this idea: during inference, multiple augmented versions of a sample are evaluated, and the final prediction is aggregated. If the original label conflicts with the majority of augmented predictions, it signals potential noise. This method is particularly useful in active learning pipelines, where uncertain or conflicting predictions guide relabeling efforts.
Finally, combining data augmentation with noise-robust algorithms enhances resilience to label errors. For example, MixUp—a technique that blends pairs of images and their labels—can dilute the impact of individual noisy labels by averaging them with others. Similarly, co-teaching frameworks train two models simultaneously, where each model selects data it deems “clean” based on agreement with the other. Augmentation expands the pool of candidate samples, improving the chances of identifying reliable examples. In text tasks, back-translation (translating text to another language and back) generates paraphrased versions, which can help models distinguish between true linguistic patterns and label noise. By integrating augmentation with these strategies, developers create systems that learn robust features while naturally suppressing the effects of label errors.