Data augmentation: A comprehensive survey of modern approaches

To ensure good performance, modern machine learning models typically require large amounts of quality annotated data. Different from previous surveys, we cover a more extensive array of modern techniques and applications.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 Meanwhile, the data collection and annotation processes are usually performed manually, and consume a lot of time and resources.
• 2 The quality and representativeness of curated data for a given task is usually dictated by the natural availability of clean data in the particular domain as well as the level of expertise of developers involved.
• 3 In many real-world application settings it is often not feasible to obtain sufficient training data.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: