Data Augmentation
Data Augmentation is the practice of artificially increasing the size and diversity of a training dataset by applying transformations (like cropping, rotating, flipping, or paraphrasing) to existing data points.
Frequently Asked Questions
How does data augmentation prevent overfitting?▼
By presenting slightly different variations of the inputs, it prevents the model from memorizing specific training pixels or tokens.
Can you use LLMs for data augmentation?▼
Yes, LLMs are frequently used to generate paraphrased variants of sentences to expand text training datasets.
Quick Facts
- CategoryModel Training
- Key ApplicationImage model training, synthetic text expansion, and overfitting prevention
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