Data augmentation

As explained in Generative AI in Action

Data augmentation is the creation or transformation of training examples to increase dataset variety while preserving task-relevant information.

Data augmentation creates or transforms training examples to increase dataset variety while preserving task-relevant information. In vision applications, it can address scarce data, limited diversity, and rare edge cases. section 4.1 section 4.1.2

GAN-based augmentation is useful when:

• Training data is insufficient for a target visual task. section 4.1.2
• Rare edge cases need additional examples. section 4.1.2
• Training data lacks the diversity needed for realistic scenarios or simulations. section 4.1 section 4.1.2

Training simulations

An education system can use augmented visual data to create realistic and diverse training scenarios based on learner progress, supporting interactive simulations. section 4.1

Figure 4.5 GAN model architecture

Inspect the GAN architecture to see the generator, discriminator, latent space, and fine-tuning relationship behind synthetic-data generation. section 4.1.2

Validate generated examples carefully: GANs may repeat outputs through mode collapse, behave unstably, offer limited control, or reproduce training-data bias. Generated content can also create privacy, misinformation, and misrepresentation risks. section 4.1.2

How Generative AI in Action covers it

How deep it goes

Explains GAN-based synthetic-data augmentation and names mode collapse, instability, output control, and bias risks without implementation steps.

Examples

Uses education training simulations, rare edge cases, and datasets lacking sufficient diversity as augmentation scenarios.

What you can do after

Apply it via GANs to create synthetic training data for edge cases or scarce, insufficiently diverse datasets.

How it connects

Connects data augmentation with GANs, VAEs, image generation, style transfer, and super-resolution.

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Also explained in

• 

  liveBookmanning.com
  Deep Learning with R, Third EditionData augmentation is a technique that generates additional training examples by applying label-preserving transformations to existing data.
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  Deep Learning with PyTorch, Second EditionA training technique that creates modified versions of examples to increase effective dataset diversity while preserving task-relevant meaning.
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  Deep Learning with Python, Third EditionA regularization approach that creates additional training examples by applying label-preserving random transformations to existing samples.
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  Delivering Machine Learning Projects: From design to deploymentData augmentation is the creation of additional training examples by applying transformations or alterations to existing data.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA technique that expands or diversifies training data by applying label-preserving transformations or generating additional examples.
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  Deep Learning with JAXThe practice of creating modified training examples to increase diversity and improve model robustness.
• 

  liveBookmanning.com
  Inside Deep Learning: Math, Algorithms, ModelsThe practice of creating additional training examples by applying label-preserving transformations to existing data.
• 

  liveBookmanning.com
  Sutskever's ListData augmentation is the creation of varied training examples by applying label-preserving transformations to existing data.

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Related concepts

• AlexNetused by · AlexNet is a convolutional neural network architecture designed for image classification and recognized for its landmark performance in large-scale image recognition.
• overfittingaddresses · Overfitting is a modeling condition in which a model fits patterns or idiosyncrasies in its training data too closely and consequently generalizes poorly to new data.
• training datasetproduces · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
• demonstrationuses · A demonstration is an example pairing an input, request, or prompt with a corresponding output, answer, or label to illustrate a task or expected behavior.
• lax.switchuses · A JAX control-flow primitive that selects one branch from multiple alternatives based on an integer index.
• regularizationkind of · Regularization is a modeling technique that constrains or penalizes solutions during fitting or optimization to discourage undesirable complexity or behavior and improve generalization or estimation.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.