ML0017 Data Augmentation

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What are the common data augmentation techniques?

Answer

Data augmentation increases the diversity and effective size of a training set by creating modified versions of the existing data, especially valuable in computer vision and NLP, where collecting and labeling new data is expensive. In computer vision, common techniques are geometric transformations (rotate, flip, crop, scale), color adjustments (brightness, contrast, saturation, color jitter), and noise injection (random noise or blur). In NLP: synonym replacement, back translation (translate to another language and back), and random insertion or deletion of words. For tabular data: SMOTE-style synthetic sample generation and small random noise on numeric features. The benefits: better robustness and generalization, less overfitting (the model cannot memorize a moving target), relief for class imbalance, and lower data-collection cost.

(1) Core Idea: Apply label-preserving transformations so one sample teaches many variations of the same concept.
(2) By Data Type: Geometric/color/noise for images, synonym/back-translation/edit for text, SMOTE/noise for tables.
(3) Benefits: Robustness, regularization against overfitting, imbalance relief, and cost savings, all from data you already have.

One original image and five augmented variants: flip, rotate, crop, brightness, noise

Figure 1: One sample, five augmented views. The label never changes (only the appearance does), so the model learns invariance instead of memorizing pixels.

Mathematical Formulation:
$\tilde{x} = T(x; \phi), \quad \tilde{y} = y$
$\min_\theta \; \mathbb{E}_{(x,y)} \, \mathbb{E}_{\phi} \, \ell\big(f(T(x;\phi);\theta), y\big)$

Where:

$T(x;\phi)$ is the augmentation transform applied to sample $x$ with random parameters $\phi$ (e.g., rotation angle, crop offset).
$\tilde{x}$ is the augmented sample and $\tilde{y}$ its label, unchanged because the transforms are label-preserving.
$f(\cdot;\theta)$ is the model with parameters $\theta$ , and $\ell$ is the training loss.
$\mathbb{E}_{\phi}$ is the expectation over random augmentations: training minimizes loss over infinitely many variants, which is what regularizes the model.