Training & Fine-tuning

Data Augmentation

Artificially expanding training data by creating variations of existing examples.

Definition

Data augmentation involves creating modified versions of existing training examples to increase the size and diversity of the training dataset. For images, this might mean flipping, rotating, or changing the colour. For text, this might mean paraphrasing, translating and back-translating, or adding noise. This helps models generalise better and reduces overfitting, particularly when you don't have enough labelled examples to train effectively.

Related Terms

Training Data

The text, documents, or other content an AI learns from during its development.

Synthetic Data

AI-generated training data used when real data is scarce, sensitive, or expensive to label.

Overfitting

When a model memorises training data rather than learning general patterns.

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Disclaimer

This definition is provided for educational and informational purposes only. It represents a general explanation of a technical concept and does not constitute professional, technical, or investment advice. Artificial intelligence is a rapidly evolving field; terminology, techniques, and capabilities change frequently. Coaley Peak Ltd makes no warranty as to the accuracy, completeness, or currency of the information provided. Nothing on this page should be relied upon as the sole basis for commercial, technical, legal, or investment decisions without independent professional advice.

Document reference: ISO_webpage_knowledge-base_glossary_v1

Last modified: 29 March 2026

Knowledge Base·Training & Fine-tuning·Data Augmentation