Synthetic Data Generation for Beginners: Part One
By Abdullah Hassan
In the rapidly evolving field of artificial intelligence, one of the most fascinating advancements has been the development of Generative Adversarial Networks (GANs). Introduced by Ian Goodfellow and his colleagues in 2014, GANs have revolutionized the way machines understand and generate data, particularly in the realm of images, music, text, video and more recently, the generation of synthetic tabular datasets.
At its core, a GAN consists of two neural networks engaged in a continuous game of cat and mouse. The first, known as the Generator, learns to produce content that is indistinguishable from real-world data. The second, the Discriminator, assesses whether the data it receives is genuine or produced by the Generator. Through this adversarial process, both networks continually learn and improve, with the Generator striving to produce increasingly convincing 'fakes', and the Discriminator becoming better at distinguishing between real and generated content.
This dynamic process results in the Generator's ability to produce highly realistic data, which has profound implications across various fields. From creating photorealistic images that never existed, designing virtual environments, and generating realistic speech for virtual assistants, to advancing drug discovery and creating new artworks, the applications of GANs are as diverse as they are impactful.
However, the power of GANs also brings ethical and societal challenges, especially in the context of deep fakes, privacy, and security. As we continue to explore the capabilities and potential of new machine-learning techniques, it is essential to continuously learn and improve upon our understanding of the underlying models and technologies that are leveraged to create our futures. This is the first of a series of articles which are being written and updated by our AI research team within Unamani AI to contribute towards a greater public understanding and awareness of what AI models can do and also begin the conversation on where we need to intervene and redirect our focus (as a species) towards a brighter and more enlightened future.
 What is Synthetic Data?
Synthetic data refers to artificially generated data that mimics the characteristics of real-world data, without containing any identifiable information. It is created using algorithms and statistical models to produce datasets that closely resemble authentic data sets. The beauty of the underlying model is the fact that, once trained, it can generate synthetic data, which is almost indistinguishable from the original dataset, with only random noise as an input.
Synthetic data is generated through a process of data augmentation, where existing datasets are manipulated and transformed using statistical methods. It's crucial for testing and training models without risking the privacy of real individuals or breaching data regulations.Â
Synthetic data also helps bridge the gap between the data science teams and the business side of an organisation. By generating realistic datasets, it is easier for industries to perform experiments and simulations that are more inclusive and representative of real-world scenarios.Â
The Synthetic Data Generation Market is anticipated to increase from USD 0.3 billion in 2023 to USD 2.1 billion by 2028, with a CAGR of 45.7%.
Advantages of Synthetic Data
Enhanced Data Privacy and Security: One major advantage of synthetic data is that it can be used to enhance the security and privacy of those from whom the real data was obtained. Synthetic data does not contain any personal information; it is sample data that has a distribution similar to the original data. These synthetic datasets can be shared and collaborated on safely without the burden of bureaucracy, dangers to privacy and loss of data utility.
Cost-Effectiveness in Data Generation:Â In comparison to real data, synthetic data is more affordable and a cost-effective solution for data-hungry projects. Traditional data collection and storage methods are costly, resource-intensive and time-consuming. By using synthetic data, businesses significantly reduce the costs associated with data collection, storage and analysis.Â
Increased Data Diversity and Volume:Â A main advantage of synthetic data is that it can be generated in large quantities with different characteristics to meet the specific needs of businesses. Synthetic data is created by generative AI algorithms, which can be instructed to create larger, smaller, unbiased or richer versions of the original data. Resulting in diverse datasets that can be used to train machine learning models.Â
Uses of Synthetic Data
Synthetic data is a powerful tool that is used in various industries but is mostly used in artificial intelligence and machine learning. It allows researchers and practitioners to explore solutions without the challenges of real-world data such as bias, incompleteness, and a lack of variety.
Synthetic data finds applicability in a variety of situations. Sufficient, good-quality data remains a prerequisite when it comes to machine learning. At times, access to real data might be restricted due to privacy concerns, while at times it might appear that the data isn't enough to train the machine learning model.
Sometimes, synthetic data is generated to serve as complementary data, which helps in improving the machine learning model. Many industries can reap substantial benefits from synthetic data:
Banking and financial services
Healthcare and pharmaceuticals
Automotive and manufacturing
Robotics
Internet advertising and digital marketing
Intelligence and security firms
Types of Synthetic Data
While opting for the most appropriate method of creating synthetic data, it is essential to know the type of synthetic data required to solve a business problem. Fully synthetic and partially synthetic data are the two categories of synthetic data.
Fully synthetic data does not have any connection to real data. This indicates that all the required variables are available, yet the data is not identifiable.
Partially synthetic data retains all the information from the original data except the sensitive information. It is extracted from the actual data, which is why sometimes the true values are likely to remain in the curated synthetic data set.
Varieties of Synthetic Data
Here are some varieties of synthetic data:
Text data: Synthetic data can be artificially generated text in natural language processing (NLP) applications.
Tabular data: Tabular synthetic data refers to artificially generated data like real-life data logs or tables useful for classification or regression tasks.
Media: Synthetic data can also be synthetic video, image, or sound to be used in computer vision applications.
Synthetic Data Generation Methods
For building a synthetic data set, the following techniques are used:
Based on the statistical distribution:
In this methodology, practitioners are required to derive numerical values from the distribution by analysing actual statistical distributions, thereby generating data that mirrors authentic datasets. This technique becomes particularly invaluable in scenarios where access to real data is constrained, allowing for the utilization of synthetically generated yet factually consistent data.
When a data scientist possesses a comprehensive grasp of the statistical distribution inherent within real-world data, they are equipped to construct a dataset that embodies a random sampling from this distribution. This objective can be accomplished through various statistical models, including but not limited to, the normal distribution, chi-square distribution, and exponential distribution. The effectiveness and accuracy of models trained using this approach are significantly influenced by the data scientist’s proficiency and understanding of these statistical methodologies.
Based on an Agent-To-Model
With this technique, you can make a model that helps understand why things happen the way they do by building an understanding of the behaviour of underlying variables as well as an understanding of the underlying interactions between variables, thus allowing it to create new data that looks just like the real thing.
Other ways of learning from data can also work for matching patterns, but if you're trying to guess what happens next, some methods, like decision trees, might guess too much if they're not used carefully.
Sometimes, you might have some real data to start with. In these cases, companies can mix real data with fake data by looking at data patterns and also by pretending how things might happen in the real world, making a blended set of data that's both realistic and useful.
Using Deep Learning
The use of deep learning models which will employ a Variational autoencoder or Generative Adversarial Network model uses methods for generating synthetic data.
VAEs are unsupervised machine learning model types that contain encoders to compress and compact the actual data while the decoders analyse this data for generating a representation of the actual data. The vital reason for using VAE is to ensure that both input and output data remain extremely similar.
GAN models and adversarial networks are two competing neural networks. GAN is the generator network that is responsible for creating synthetic data. An adversarial network is the discriminator network, which functions by determining a fake dataset and the generator is notified about this discrimination. The generator will then modify the next batch of data. In this way, the discriminator will improve the detection of fake assets.
Challenges and limitations while using synthetic data
While synthetic data presents numerous benefits for enterprises embarking on data science projects, it is imperative to acknowledge its inherent limitations:
Data Reliability: The axiom that the efficacy of machine learning or deep learning models is contingent upon the quality of their data inputs holds particularly true here. The fidelity of synthetic data is intrinsically linked to both the quality of the input data and the sophistication of the generation model. Ensuring the absence of bias in the source data is critical, as any such biases are likely to be replicated in the synthetic output. Moreover, rigorous validation and verification processes are essential to ascertain the data's suitability for predictive tasks.
Replication of Outliers: Although synthetic data can approximate real-world phenomena, at this point, it can not perfectly replicate the phenomena. Consequently, this may lead to the omission of critical outliers present in the actual data, which could possess significant analytical value.
Resource Intensiveness: Despite the relative ease and cost-effectiveness of producing synthetic data compared to sourcing real-world data, this process demands considerable expertise, as well as substantial time and effort investments.
User Acceptance: Given that synthetic data is a relatively novel concept, there may be a degree of skepticism regarding its reliability for making informed predictions. Thus, fostering awareness and understanding of synthetic data's benefits is crucial for enhancing its acceptance among potential users.
Quality Assurance and Control: The primary objective behind generating synthetic data is to closely emulate real-world datasets. This necessitates meticulous manual inspection to ensure the integrity and accuracy of the data, especially for complex datasets created through automated algorithms. Such scrutiny is vital to guarantee the data's applicability in enhancing the performance of machine learning or deep learning models.
In numerous scenarios, synthetic data emerges as a viable solution to overcome the constraints of data scarcity or the absence of pertinent data within enterprises or organizations. We have explored various methodologies for generating synthetic data and we have examined the inherent use cases and challenges associated with employing synthetic data.
While real data is invariably the gold standard for informed business decision-making, synthetic data stands as a formidable alternative in contexts where access to authentic raw data is impeded. It is imperative to acknowledge, however, that the production of synthetic data necessitates the expertise of data scientists well-versed in data modelling techniques. Furthermore, a profound comprehension of the original data and its contextual environment is essential. This ensures the synthetic data produced bears a close resemblance to the actual data, thereby enhancing its utility for analytical purposes.
Author: Abdullah Hassan
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