Standard 3 stops to get here · leads to 4

Data Augmentation

Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization.

Your route here

3 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. 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.

  3. 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.

  4. Data Augmentation · you are here ✓ understood

Where it sits

Before this

Training Data Overfitting
Data Augmentation

Leads to

Image Augmentation CutMix Cutout Mixup

Explore nearby

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 Any change to training meant to make a model generalize better rather than fit its training data more closely, such as weight penalties, dropout or early stopping. Training CutMix Data augmentation combining image patches and labels from two examples, improving robustness.