Definition
This method artificially expands the training dataset by creating modified versions of existing samples, such as rotating images, adding noise to audio, or synonym replacement in text. It helps prevent overfitting by exposing the model to a wider variety of scenarios during training, thereby improving generalization performance. It is particularly crucial in domains where collecting large amounts of real-world labeled data is expensive or difficult.
Summary
Data augmentation is a technique used to increase the diversity and size of training datasets by applying transformations to existing data points.
Key Concepts
- Overfitting Prevention
- Dataset Expansion
- Generalization
- Transformations
Use Cases
- Improving computer vision model robustness
- Enhancing NLP model performance with limited text
- Balancing imbalanced datasets
Code Example
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