Data augmentation: A comprehensive survey of modern approaches

🤖 Plain-English Summary

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.

🔑 Key Findings

• Meanwhile, the data collection and annotation processes are usually performed manually, and consume a lot of time and resources.
• 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.
• In many real-world application settings it is often not feasible to obtain sufficient training data.

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