Synthetic data is like creating a realistic yet fictional version of actual data. This isn't just about hiding or changing sensitive details but making entirely new data that looks real but can't be linked back to any real data. These methods can make all sorts of data, like numbers, categories, or yes/no answers, and they can make as much of it as needed.
The cool part is you can adjust how much the synthetic data looks like the real thing while keeping people's information private. We're really focused on keeping people's information safe, especially when sharing data. We assume that even if someone could see the synthetic data and how it's made, they couldn't get to the real data behind it.
Let's talk about why making synthetic data is a big deal:
Working Around Data Sharing Rules: Occasionally, regulatory constraints may inhibit the dissemination of data within an organisation. Alternatively, there may be instances where a team is inclined to initiate data analysis or projects in the absence of formal authorization.
Not Enough Past Data:In scenarios where historical data is insufficient for analysing events such as abrupt stock market crashes or economic downturns, synthetic data proves invaluable for validating theories or testing strategies.
Fixing Skewed Data: In contexts such as fraud detection, data distribution often exhibits significant skewness. Employing synthetic data that mirrors real-world patterns can effectively mitigate this imbalance, enhancing the efficacy of detection methodologies.
Training Advanced AI: Training state-of-the-art AI models demands a substantial volume of data and computational resources. Synthetic data emerges as a viable solution when real data is unavailable, serving as a preventive measure against potential reverse-engineering of sensitive AI data.
Sharing Data Safely: The dissemination of synthetic data enables institutions to collaborate effectively, fostering the development of enhanced solutions to financial challenges, all while adhering to regulations prohibiting the sharing of real data.
In this article, we delve further into the realm of synthetic data, exploring the characteristics of the generated data, the underlying machine learning techniques behind the generation of the synthetic data (i.e., Generative Adversarial Networks (‘GANs’)) as well as the limitations behind GANs. This discussion aims to equip you with the requisite knowledge to effectively produce, utilise and understand synthetic data in addressing data-centric challenges.
Characteristics of Synthetic Data
Data scientists aren't concerned about whether the data they use is real or synthetic. The quality of the data, with the underlying trends or patterns, and existing biases, matters more to them.
Here are some notable characteristics of synthetic data:
Improved data quality: Real-world data, other than being difficult and expensive to acquire, is also likely to be vulnerable to human errors, inaccuracies, and biases, all of which directly impact the quality of a machine learning model. However, companies can place higher confidence in the quality, diversity, and balance of the data while generating synthetic data.
Scalability of data: With the increasing demand for training data, data scientists have no other option but to opt for synthetic data. It can be adapted in size to fit the training needs of the machine learning models.
Simple and effective: Creating fake data is quite simple when using algorithms. But it is important to ensure that the generated synthetic data does not reveal any links to the real data, that it is error-free, and does not have additional biases.
Data scientists enjoy complete control over how synthetic data is organised, presented, and labelled. That indicates that companies can access a ready-to-use source of high-quality, trustworthy data with a few clicks.
What are GANs?
GANs, or Generative Adversarial Networks, are a type of deep learning model that creates synthetic data resembling real data. They involve two competing neural networks: a generator that creates the synthetic data and a discriminator that tries to tell apart real data from the generated one.
Through their training, these networks compete, leading to improvements in both. The generator aims to produce realistic data that neither the discriminator nor humans can easily distinguish from actual data.
GANs are particularly known for their ability to create realistic images and videos, among other applications. A notable use is in generating lifelike images of human faces, as demonstrated with the StyleGAN2 architecture. This advancement is detailed in the study "Analysing and Improving the Image Quality of StyleGAN."
Originally introduced by Ian Goodfellow and colleagues, GANs have garnered substantial interest within the machine learning community. Yann LeCun hailed GANs as "the most interesting idea in the last 10 years in Machine Learning," while Andrew Ng acknowledged them as a pivotal advancement in the field.
Types of Data GANs Can Generate:
Image Data: Beyond creating human faces, GANs can perform image-to-image translation, changing the style of an image while keeping its content the same. This means taking an image from one style and transforming it into another style. For example, a project by Microsoft Research Asia and the University of Science and Technology of China introduced a method to smoothly transition between styles using an "auxiliary domain," ensuring consistent translations across different styles, as explained in their paper "Image-to-Image Translation with Multi-Path Consistency Regularisation."
Tabular Data: GANs can generate synthetic tabular data that maintains the statistical characteristics of the original data but with enhanced privacy. This approach, described in "Data Synthesis based on Generative Adversarial Networks," allows for the creation of table-GAN. It synthesises data that are statistically similar to the original yet minimises the risk of leaking sensitive information. This process involves balancing privacy protection with the synthetic data's utility for training machine learning models.
Sound and Speech Data: In the realm of audio, GANs have been applied to generate human-like speech from text. The GAN-TTS model, detailed in "High Fidelity Speech Synthesis with Adversarial Networks," produces realistic speech audio. This model pays attention to linguistic and pitch accuracy, making the generated speech nearly indistinguishable from genuine human speech.
Examples of such generated speech are available online, showcasing the model's capability.
GANs, through these diverse applications, demonstrate a remarkable ability to generate synthetic data across various domains, offering potential advancements in privacy, realism, and machine learning model training.
Real-world applications using Synthetic data
Here are some real-world examples where synthetic data is being actively used:
Healthcare: Healthcare organisations use synthetic data to create models and a variety of dataset testing for conditions that don’t have actual data. In the field of medical imaging, synthetic data is being used to train AI models while always ensuring patient privacy. Additionally, they are employing synthetic data to forecast and predict trends of diseases.
Agriculture: Synthetic data is helpful in computer vision applications that assist in predicting crop yield, crop disease detection, seed/fruit/flower identification, plant growth models, and more.
Banking and finance: Banks and financial institutions can better identify and prevent online fraud as data scientists can design and develop new effective fraud detection methods using synthetic data.
eCommerce: Companies derive the benefits of efficient warehousing and inventory management as well as an improved customer online purchase experiences through advanced machine learning models trained on synthetic data.
Manufacturing: Companies are benefiting from synthetic data for predictive maintenance and quality control.
Disaster prediction and risk management: Government organisations are using synthetic data for predicting natural calamities for disaster prevention and lowering the risks.
Automotive & Robotics: Companies make use of synthetic data to simulate and train self-driving cars/autonomous vehicles, drones, or robots.
Future of synthetic data
This article has explored various techniques and advantages of synthetic data. Now, the question arises: Will synthetic data replace real-world data? Is synthetic data the future?
Synthetic data indeed offers scalability and intelligence surpassing that of real-world data. However, generating accurate synthetic data demands more effort compared to using AI tools. Achieving precision in synthetic data creation necessitates deep knowledge of AI and specialised skills in managing complex frameworks.
Moreover, datasets should remain devoid of any trained models that could skew their representation, distancing them from reality. By addressing biases and ensuring a true reflection of real-world data, synthetic data generation can align with desired objectives.
Synthetic data aims to empower data scientists in pursuing novel and innovative endeavours that may prove challenging with real-world data alone. Hence, it's plausible to envision synthetic data as a pivotal component of the future data landscape.
Wrapping up
In numerous scenarios within business or organisational settings, synthetic data emerges as a crucial tool to overcome data shortages or the absence of relevant datasets. We have examined various techniques for generating synthetic data and identified the beneficiaries of such practices.
Additionally, we have addressed challenges associated with working with synthetic data, alongside real-world instances of its application across industries.
While real data remains the preferred choice for informed decision-making in business contexts, synthetic data serves as a valuable alternative when genuine raw data is unavailable for analysis.
Nonetheless, it's imperative to acknowledge that generating synthetic data necessitates the expertise of data scientists proficient in data modelling. Moreover, a profound understanding of the genuine data and its contextual nuances is indispensable to ensure the synthetic data closely mirrors reality.
Author: Abdullah Hassan
Email: abdullah.hassan@unamani.com
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