Term
Data Augmentation (ˈdeɪtə ˌɔːɡmenˈteɪʃən)
Definition
Data Augmentation involves modifying existing examples in a dataset to create new, varied versions. This process expands and diversifies training sets and improves the performance of AI models by exposing them to a broader range of data scenarios.
Where you’ll find it
Data augmentation is typically employed during the preprocessing stage of AI model training. This occurs before the data is fed into the training algorithm. It's a common practice across various AI platforms, especially in those dealing with image, text, and audio data.
Common use cases
- Enhancing model performance by training with a more diverse set of data examples.
- Preventing overfitting, where a model performs well on training data but poorly on unseen data.
- Improving the accuracy of predictions in AI models by providing a richer training environment.
Things to watch out for
- Over-augmentation: Applying too many augmentations can distort data, making it unhelpful for training.
- Irrelevance: Ensure the augmentations reflect variations that could actually occur in real-world data.
- Balance: Maintain a balance between original and augmented data to avoid biasing the model towards augmented data.
Related terms
- Preprocessing
- Training Sets
- Model Overfitting
- Model Generalization
- Machine Learning Algorithms