DataAugmentation

Data augmentation is a set of techniques used to increase the diversity of data available for training models without collecting new data. By applying carefully chosen transformations to existing samples, augmentation aims to improve the robustness and generalization of learning algorithms, reduce overfitting, and help models perform better on unseen data. In supervised learning, augmented data is typically labeled the same as the source sample.

The approach is widely used across domains such as computer vision, natural language processing, and audio processing. In vision, common transformations include geometric methods like flipping, rotation, translation, scaling, and cropping, as well as photometric changes such as color jitter, contrast adjustments, blur, and noise. In NLP, augmentation often relies on linguistic perturbations such as synonym replacement, random insertion or deletion, back-translation, and paraphrase generation. In audio, time-domain and frequency-domain techniques such as adding noise, pitch shifting, time-stretching, and masking are used.

Data augmentation can be applied offline, by expanding the training dataset ahead of time, or online, generating augmented samples on the fly during training. Generative approaches using models like variational autoencoders or generative adversarial networks can produce synthetic samples beyond simple transformations. Other methods include mixup, cutout, and random erasing, as well as domain-specific augmentations.

Key considerations include ensuring that transformations preserve the label, avoiding introduction of artifacts that mislead the model, and balancing augmentation with real data to prevent bias. Computational overhead and memory usage should be managed, and augmented data should be validated to avoid data leakage from test to training. Evaluation often involves ablation studies to assess the impact of augmentation policies on performance.