Beginner · Computer Vision

Data Augmentation

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. Techniques that artificially expand training datasets by applying transformations to existing samples.

Technical Definition

Techniques that artificially expand training datasets by applying transformations to existing samples.

How it works

Data augmentation creates modified copies of training data. For images: rotation, flipping, cropping, color jittering. For text: synonym replacement, back-translation. Advanced methods like Mixup blend samples and CutMix pastes patches. Augmentation acts as a regularizer, reducing overfitting.

Related Concepts

• Convolutional Neural Network (CNN) — A neural network that uses learnable filters to detect spatial patterns like edges, textures, and objects in images.
• Overfitting — When a model learns noise and specific patterns in training data too well, causing it to perform poorly on new, unseen data.
• Regularization — Techniques that constrain a model's complexity to prevent overfitting and improve generalization to unseen data.