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February 28, 2024

Generative AI for Data Augmentation: How to Use It


Artur Haponik

CEO & Co-Founder

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7 minutes

Machine learning models require vast amounts of training data to be accurate and effective. This data has to be representative of the models’ purpose and up-to-date to prevent redundancies and inaccuracies. Unfortunately, a huge chunk of data becomes inaccurate every year, leading to data quality concerns among developers. In a bid to curb this issue and create more accurate models with the data they have on hand, developers have recently started leveraging Generative AI models for data augmentation. By leveraging neural networks and cutting-edge algorithms, generative AI models can effectively create synthetic data instances that closely mimic the characteristics of real-world samples.

This article will explore the role of generative AI in data augmentation and synthetic data generation in enhancing the quality and quantity of training data.


Generative AI for data augmentation

Data augmentation with Generative AI is the process of utilizing artificial intelligence (AI) algorithms to create new synthetic data points that can be added to existing datasets. This unique approach to sourcing training data is commonly used in deep learning and machine learning applications to improve the performance and accuracy of models by increasing the amount and diversity of training data.


By generating new synthetic data points that are similar to the original data, data scientists and developers can effectively overcome the challenges of limited or imbalanced datasets.


In that regard, GenAI models, like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown great promise in generating high-quality synthetic data. These models typically learn the underlying characteristics and distribution of input data and use the information to generate new samples that closely resemble the original data.

Basics of data augmentation and synthetic data generation

Data augmentation and synthetic data are some of the most commonly used techniques in improving the quality of the training data used in developing machine learning models.

Data augmentation involves performing transformations such as flipping, cropping, color adjustments, and rotations to existing datasets to create modified versions of the original data.

By creating modified versions of existing data and then adding it to existing datasets, developers can effectively introduce diversity and viability in the dataset, making the model more robust and less prone to overfitting. This technique is commonly used in computer vision tasks like object detection and image classification.

Synthetic data generation, on the other hand, involves creating entirely new data points by utilizing statistical modeling and other algorithms. The samples are generated in such a way that they mimic the patterns and characteristics of the real data, thus significantly expanding the size of the training dataset. Besides addressing data scarcity issues, synthetic data can also come in handy when obtaining real data is expensive, difficult, or time-consuming.

Data augmentation benefits in AI

Data augmentation is vital in the development of machine learning models, particularly in instances where developers need to expand the training dataset by applying various transformations to original data. As such, its ultimate goal is to create new data instances that retain the features and characteristics of the original samples while introducing variability and diversity.

Data augmentation with Generative AI presents numerous benefits that enhance the performance of machine learning models. Some of the most notable benefits of data augmentation in machine learning and deep learning model development include:

Reduced data collection efforts

Each person on the internet produces about 1.7 MB of data every second, and that does not account for organizational data. [1] Unfortunately, most of this data is in unstructured form and may require further filtering, analysis, and labeling to facilitate model training.

This fact alone makes data collection efforts a costly and time-consuming endeavor. However, by leveraging data augmentation and synthetic data generation, developers and data scientists can effectively maximize the use of existing data, thus reducing the need for extensive data collection efforts.

Enhanced model performance

Data augmentation with generative AI introduces variations that mimic real-world scenarios. This makes the trained machine learning model more robust and capable of handling a myriad of input variations, such as changes in angles, lighting conditions, and backgrounds.

Read more about Generative AI in data engineering: Generate synthetic data to improve accuracy in ml models

Better utilization of resources

According to a report by Algorithmia, it takes an average of between 8 to 9 days to deploy a machine learning model. [2] Some models may take even longer to build and deploy depending on size, complexity, and developers’ experience.

By utilizing augmented data, developers and data scientists can leverage parallel processing techniques, leading to faster optimization and convergence. This ultimately speeds up the model development process.

Improved model generalization

One of the greatest challenges in developing a machine-learning model is overfitting. Overfitting occurs when a model gives accurate predictions for training data but fails to replicate the results with new data. [3]

By exposing the model to a more diverse and extensive dataset created through augmentation techniques, developers can effectively deploy models that can generalize better and be more resilient to overfitting the original dataset.

Diversifying data with Generative AI

As stated earlier, GenAI algorithms create synthetic data by learning structures and patterns from existing data. These algorithms model the underlying distribution of the original data sample, enabling the generator part of the generative AI model to generate new instances that resemble the original dataset.

For Generative Adversarial Networks (GANs), the generator creates synthetic data where the discriminator evaluates the authenticity of the data. By leveraging adversarial training, the generator improves its ability to generate more realistic samples that can ‘fool’ the discriminator.

Variational Autoencoders (VAEs), on the other hand, focus primarily on learning latent representations of the original dataset and generating new samples by sampling from the data’s latent space.

Synthetic data generated through data augmentation can augment limited datasets, enhance privacy by reducing private information, and balance class distributions. It also improves model training by improving generalization and providing diverse and representative data, thus improving the model’s performance on real-world tasks.

The future of Generative AI in data augmentation

Generative AI in data augmentation has a promising future. Advancements in machine learning and deep learning capabilities will allow the development of more sophisticated AI models that can generate more realistic synthetic data that will be indistinguishable from real data.

Ultimately, this will facilitate broader and safer use cases in various applications, including autonomous vehicles, AI-driven medical imaging technologies, and natural language processing. Using gen AI for synthetic data generation will also alleviate the need to source extensive datasets for model training, thus reducing the overall cost of model development and deployment.

Data augmentation: Classic vs. Generative AI

Traditional/classic data augmentation methods typically involve using simple techniques, like cropping, flipping, and rotation for image data. These techniques must be performed independently, thus increasing the overall cost and resource-intensiveness of the project.

On the other hand, Generative AI offers a more advanced and context-aware approach to data augmentation by leveraging AI algorithms that are specially designed to learn from existing datasets and generate synthetic data that resembles the original sample.

Based on this comparison, it’s clear to see that Generative AI offers a more holistic approach to data augmentation. It also facilitates the creation of more accurate synthetic data by taking human error out of the equation.


Wrapping up

GenAI is revolutionizing data augmentation. The once time-consuming and resource-intensive process can now be carried out in a fairly short amount of time. It has also helped solve several bottlenecks associated with traditional data augmentation methods, such as data quality, computational resources, and ethical concerns, particularly around privacy.


[1] How Much Data is Produced Every Day? URL: Accessed on February 21, 2024.
[2], 2020 State of Enterprise Machine Learning. URL: Accessed on February 21, 2024
[3], What is Overfitting? URL:,on%20a%20known%20data%20set. Accessed on February 21, 204


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