Standard 3 stops to get here · leads to 5
Data Augmentation
Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization.
Your route here
3 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
- Overfitting ✓ understood
When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.
- Data Augmentation · you are here ✓ understood
Where it sits
Data Augmentation
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Vision & Multimodal Data Augmentation in Vision Creating training image variations through rotation, flipping, cropping, color jittering to improve model robustness. Vision & Multimodal Image Augmentation Applying transformations (rotation, flip, crop, color) to increase training data diversity. Foundations Synthetic Data Artificially generated data created to augment training sets, protect privacy, or simulate rare scenarios. Training Mixup Data augmentation creating synthetic examples by interpolating between training examples and their labels. Training Regularization Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping). Training CutMix Data augmentation combining image patches and labels from two examples, improving robustness.