Synthetic Data Generation Using Generative AI

17-Oct-2024

Data has transitioned from being a scarce resource to an abundant commodity, customized to the specific requirements of businesses and researchers. In a time when data privacy concerns are significant and the demand for quality datasets is catapulting, how can organizations secure sufficient good-quality data to train their machine learning models? Enter synthetic data generation, a groundbreaking technique developed through generative AI that aims to transform the data science framework.

What is Synthetic Data?

Synthetic data can be generated using deep learning algorithms, and many enterprises utilize it as a substitute for real data. As mentioned earlier, access to real data may be restricted due to compliance and privacy regulations, or it may need adjustments to meet specific objectives. The goal of synthetic data is to emulate authentic data by reconstructing its statistical properties. Once trained on actual data, a synthetic data generator can produce any quantity of data that closely resembles the patterns, distributions, and relationships found in the original dataset. This method not only facilitates the creation of similar data but also allows for the application of specific constraints as needed.

How does Generative AI help in synthetic data generation?

Generative AI is at the forefront of synthetic data generation techniques, rapidly gaining traction due to its remarkable ability to manage vast volumes and diverse data distributions. As organizations increasingly seek innovative solutions to data scarcity and privacy concerns, generative AI techniques stand out as powerful tools that not only augment existing datasets but also create entirely new ones that maintain the statistical attributes of real data. 

Let us see the key methods involved in synthetic data generation using Gen AI:

1. Generative pre-trained transformer (GPT)

One of the most interesting progress in generative AI is the development of a Generative pre-trained transformer (GPT). This language model is trained on a multitude of data types, including text and tabular data. This helps the model to understand and replicate complex patterns and structures inherent in the data. The versatility of GPT makes it suitable for various synthetic data generation tasks.  

GPT-based tools for synthetic data generation learn from the extensive training data, identifying patterns, distributions, and correlations within the dataset. For instance, if a GPT model is trained on a financial dataset that includes customer transactions, it can generate new, realistic transactions that simulate the behavior of real customers. This is particularly beneficial for machine learning tasks that require robust datasets but face challenges due to data privacy or availability.  

The strength of GPT lies in its ability to create tabular data that preserves the finer details of the original dataset. Businesses can make the most of this generated data to enrich their machine learning models without risking the exposure of sensitive information. Moreover, GPT can adapt to diverse domains, making it a flexible choice for organizations across various industries, from healthcare to finance.

2. Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are another groundbreaking technique in the realm of synthetic data generation. GANs operate through a dual-network system comprising a "generator" and a "discriminator." The generator's role is to create realistic synthetic data, while the discriminator evaluates this data against real datasets, determining its authenticity.

The unique adversarial training process between these two networks promotes the creation of highly realistic synthetic datasets. As the generator attempts to produce data that can fool the discriminator, it learns to refine its outputs incessantly, resulting in data that closely resembles the original dataset. For example, in image generation, GANs can create lifelike photographs or artwork that can be indistinguishable from actual images.

GANs are particularly valuable in fields where data availability is limited or where data acquisition is costly or time-consuming. Industries such as healthcare, autonomous driving, and entertainment can benefit significantly from GAN-generated data. For instance, in healthcare, GANs can generate synthetic patient records that maintain the statistical patterns of real patient data, facilitating researchers to conduct studies without compromising patient privacy.

3. Variable autoencoders (VAEs)

Variational Autoencoders (VAEs) represent yet another method for synthetic data generation. VAEs consist of two main components: an "encoder" and a "decoder." The encoder summarizes the characteristics and patterns of the real data into a compressed representation, while the decoder attempts to reconstruct the data from this summary. 

VAEs excel in generating synthetic data that captures the essence of the original dataset. VAEs learn to encode and decode the data and can create new, synthetic rows of tabular data that closely resemble real data. This is especially useful in scenarios where it is important to generate data with similar distributions and relationships.  

For example, in the context of customer behavior analysis, a VAE could be trained on historical customer transaction data to generate synthetic customer profiles. These profiles can then be used for various purposes, including marketing strategies, product recommendations, and sales forecasting. The generated synthetic data helps businesses experiment and innovate while safeguarding sensitive information.

4. Diffusion models 

Diffusion models have emerged as a useful technique for synthetic data generation, particularly excelling in creating high-quality images and complex data types. These models operate by simulating a two-step process: first, they transform real data into noise through a forward process, and then they learn to reverse this process, reconstructing data from noise in a series of denoising steps. This iterative refinement allows diffusion models to produce outputs that are often more realistic and detailed than those generated by other methods, such as Generative Adversarial Networks (GANs). Their flexibility makes them suitable for diverse applications, including image generation, medical imaging, and data augmentation, while offering control over the characteristics of the generated data. 

How does the process of synthetic data generation using Gen AI work?

1. Data collection

Synthetic data begins with the collection of real-world data samples. These samples provide a foundation for creating realistic synthetic datasets, ensuring that the generated data is guided by genuine patterns.

2. Model selection and training

The next step involves choosing an appropriate generative model based on the type of data needed. We have already discussed popular deep learning models including VAEs and GANs. You can also employ diffusion models and transformer-based models such as large language models (LLMs). 

Each model has unique strengths: VAEs excel at probabilistic modeling and anomaly detection, while GANs are ideal for generating high-quality images and videos. Diffusion models have emerged as leaders in producing detailed visuals, and LLMs are focused on text generation tasks.

3. Synthetic data generation

Once trained, the generative model can produce synthetic data by sampling from the learned distribution. For instance, a GAN may create images pixel by pixel, while an LLM generates text token by token. Latent space modifications can tailor the synthetic data to specific characteristics.

4. Quality assessment

Evaluating the quality of synthetic data with statistical tests such as mean, standard deviation and variance to verify the authenticity and realism of data. 

5. Iterative improvement and deployment  

Finally, synthetic data is integrated into applications for training machine learning models or testing algorithms. Continuous refinement of the generative models based on new data ensures improved quality and relevance over time.  

Conclusion

Generative AI is changing the ways synthetic data is generated. We have presented various techniques to develop synthetic data including GPT, GANs, and VAEs that address the growing challenges of data scarcity, privacy, and quality. Organizations continue to explore transformative solutions employing generative AI solutions for the creation of authentic and high-fidelity synthetic datasets.

What is synthetic data? Generative pre-trained transformer Generative Adversarial Networks