How to use synthetic data in ML development?

Toloka Team
by Toloka Team

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Machine learning models need a large amount of data to be properly trained. But what if obtaining such data is impossible or limited due to time, cost, and resources? Anonymized synthetic data comes to the rescue, the purpose of which we will explain further below.

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What is Synthetic data?

Synthetic data in ML is fictional, non-existent data that is produced by a smart machine derived from real data. It resembles real-world data without exactly replicating it, i.e. it recreates the illusion of original data that could actually exist.

Synthetic data is not a new phenomenon, because people could create data that didn't exist in the real world long before the emergence of computers. For example, they could paint a person that didn't exist in reality. However, these days it is faster and easier to apply digital resources to produce artificial pictures, textual or any other type of content to train ML models.

The demand for non-existent data in the field of AI has arisen because of the high cost of the original data required to develop AI solutions. Personal sensitive data (such as banking or medical information) is impossible to obtain, therefore one can resort to producing synthetic data through computer algorithms.

Ensuring data confidentiality

It is also challenging to obtain sensitive information since it is private, and its confidentiality should not be compromised. Synthetic data, on the other hand, helps to address the issue of privacy and confidentiality. Since it is not genuine, there is no risk of violating the privacy of personal information, such as personally identifiable information (PII), financial records, or health data. Consequently, there is no violation of privacy and security laws such as GDPR.

If real data contains private information, synthetic datasets can be employed instead to construct an alternative that preserves the statistical features of this original data but does not reveal personal information, thus there is no direct representation of real individuals.

This approach reduces the need for researchers and data scientists to directly access sensitive real-world datasets, thereby limiting the risk of unauthorized access or misuse of confidential information. Artificially generated data is beneficial for training machine learning models, testing algorithms, or exploring diverse scenarios without the risk of classified data or confidential information leaking out.

The adoption of synthetic data to protect privacy is especially critical in domains where data processing is subject to high standards of security and compliance with privacy laws, such as medical diagnostics, financial services, or research related to people's personal lives. However, it's essential to implement appropriate safeguards and validation processes to guarantee that synthetic data effectively mirrors the genuine phenomena behind them while respecting confidentiality measures.

Types of Synthetic Data

To understand how synthetic data is applied in practice, we need to understand that it is not synonymous with randomized or augmented data. Randomized means shifting or moving the data in the set, while augmented means adding slightly modified pieces of data to the set. Furthermore, there exist three categories of synthetic data:

  • Fully synthetic data. This dataset is entirely generated and does not contain any original data;

  • Partially Synthetic Data. In these types of datasets, values or attributes that might compromise data security and privacy are eliminated from the real data and substituted with synthetic equivalents;

  • Hybrid Synthetic Data. This dataset combines both real and synthetic data, with some portions being synthetic while others remain genuine.

Some machine learning applications, like those with a high risk of inflicting harm to human beings, should better be trained not only on synthetic but also on real data, for example, self-driving car software. This is because observing real-life scenarios helps to clarify the unique behavior of drivers, pedestrians, cyclists, and other road users.

Real data reflects the complexities and nuances of the environment in ways that synthetic data often cannot fully replicate. In the case of self-driving cars, real-world data includes changes in weather conditions, road surfaces, traffic patterns, pedestrian behavior, and unforeseen scenarios that are difficult to replicate accurately.

How to Use Synthetic Data When Developing Your ML Solution?

Synthetic Data to Train AI models

Synthetic data offers a powerful tool for enhancing AI/ML model training. Synthetic data generation techniques enable the creation of additional training examples and augmentation of existing datasets beyond what is available in the real-world dataset. This expansion of the training dataset helps expose the model to a wider range of scenarios, variations, and edge cases, leading to better generalization and improved performance on unseen data.

Also, the process of generation can be designed to upsample uncommon patterns or occurrences in the data, thereby providing the model with more exposure to these rare events. First, it's crucial to identify the rare events or patterns in the dataset that are underrepresented. Once they are identified, the synthetic data generation process can be designed to intentionally generate more instances of these events.

For example, in classification tasks with imbalanced class distributions, synthetically generated data can be used to balance the classes by generating additional samples for the minority class. This helps prevent the model from being biased towards the majority class and improves its ability to accurately classify examples from all classes.

Synthetic data often comes with the advantage of pre-labeled and annotated information, reducing the need for manual labeling efforts. It enables more time to be spent on training AI models rather than on data annotation, which typically takes up a very large or even a major part of the entire AI development time. In addition, data synthesis itself is likely to take less time than real data collection.

Besides increasing the speed of training data preparation, the decision to apply synthesized data in ML development will likely cost less than collecting real data, which may include expenses related to data collection equipment, personnel, logistics, and infrastructure. Synthetic data, on the other hand, is generated using algorithms, which typically have lower costs once the generation process is set up.

ML Models Testing

Synthetic data sets may also be used to test machine learning models. Synthetic test data refers to artificially generated data specifically created for AI-based applications testing purposes. It can be adjusted to incorporate cases that are less likely to occur in real data but are important for reliability testing. It allows testers to create a wide range of test cases with diverse data characteristics, covering various scenarios that may not be present in real datasets.

Synthetic Data for Computer Vision Tasks

Acquiring substantial volumes of real-world data for computer vision tasks can incur significant costs and time investments, especially when considering the need for diverse images with variations in lighting, backgrounds, and object poses. Synthetic data generation creates large and diverse datasets at a fraction of the cost and time required for collecting real-world data.

Synthetic data technology allows for the generation of large datasets with thousands or even millions of images, providing a sufficient amount of training data to efficiently train deep learning models. Unlike real-world data collection, which may be limited by factors such as availability, acquisition time, cost, or accessibility, synthetic data can be generated algorithmically, allowing for the creation of large volumes of data quickly and efficiently with minimal effort.

Artificial data containing various objects such as pedestrians, vehicles, signs, and obstacles enables the training of object detection and recognition models for self-driving vehicles. Synthetic data training data allows for the creation of realistic driving scenarios and environments, which enables comprehensive testing and validation of autonomous driving systems without the need for physical vehicles or real-world driving.

Synthetically generated labeled datasets employed in object recognition and segmentation. Data scientists can train models to accurately detect and segment objects in real-world images using synthetic images with annotated objects. Synthetic datasets containing artificial imagery can be used to augment existing image datasets.

Synthetic Data for Natural Language Processing (NLP)

Similar to computer vision, synthetic data can augment existing text datasets by generating variations of text samples. This includes adding paraphrasing or translating text to different languages, thereby increasing the diversity of the training data and improving model performance. Exposure to a variety of such synthetic data can assist NLP models in becoming more amenable to varying patterns of language use, writing styles, and linguistic nuances, essential for deploying models in real-world applications where input data can have significant variability.

Acquiring and labeling large amounts of real-world data for large language models (LLMs) training can be prohibitively expensive. Synthetic data offers a cost-effective alternative, as it can be generated at scale using algorithms, reducing the need for extensive data collection efforts.

Moreover, access to large, diverse, and annotated datasets for LLMs may be limited, particularly for niche or specialized domains. Synthetic data allows companies to produce personalized datasets that suit their individual requirements while overcoming the constraints of data availability and accessibility.

Real-world data, especially sensitive or confidential information, may not be suitable for sharing and therefore training LLMs due to privacy concerns and legal restrictions. Synthetic training data provides a privacy-preserving solution, enabling companies to train models without compromising data confidentiality.

How to Get Synthetic Data?

There are three main ways to generate synthetic data:

  • Agent-based modeling. Known agents are defined along with their attributes, behaviors, and interactions according to prescribed rules. These rules govern how agents behave individually and how they interact with each other and their environment. By simulating these interactions over time, agent-based modeling aims to generate synthetic data that exhibits similar characteristics to the original dataset;

  • Hand-engineered methods. These involve using algorithms and rules on well-researched data samples to generate artificial ones. For instance, such methods as linear interpolation or rule-based generation if there are known rules governing the data generation process;

  • Machine learning models. They can be utilized to create synthetic data by learning the underlying patterns and structures present in the original dataset and generating new data points that follow similar distributions. We'll focus on machine learning models. Several methods and techniques are commonly employed for generating synthetic data using ML-based models:

Generative Adversarial Networks (GANs) constitute a type of deep learning architecture comprising two neural networks: a generator and a discriminator. They are trained concurrently, with the generator tasked with producing synthetic content that closely resembling real data, while the discriminator aims to differentiate between authentic and synthetic samples;

Variational Autoencoders (VAEs) represent a category of generative models crafted to encode and decode data instances within a latent space. During training, VAEs effectively learn the inherent distribution of the data, facilitating the creation of new data points through sampling from this latent space. This capability empowers VAEs to generate synthetic data that closely mirrors the original data distribution.

These generative AI models excel in producing realistic text, images, tabular data, and various other data formats.They include, for instance, foundation models like DALL-E and Midjourney for images, and LLMs like GPT and BERT for text.

Benefits of Synthetic Data Generation

  • Data Privacy and Security. The escalating worries about data breaches and privacy regulations have prompted a shift towards utilizing synthetic data as a safer alternative to real-world data. Because synthetic data is generated rather than gathered, it inherently lacks any personal or sensitive information, thereby minimizing privacy risks while preserving data quality;

  • Accelerated Generation of Cost-effective ML Training Data. Synthetic data offers a swifter and frequently more economical solution for ML training data when compared to the manual collection and labeling of real-world data. Generating data eliminates the need for expensive data collection processes, such as hiring annotators, setting up data collection infrastructure, or purchasing proprietary datasets. Once the data generation algorithms are developed, generating additional synthetic samples typically incurs minimal additional costs and takes less time, making it a cost-effective solution for acquiring data.

  • Pre-labeling. Synthetic data, generated by algorithms, already contains labels embedded within it, eliminating the need for manual annotation. This significantly reduces the time and effort required to prepare the data for tasks like training machine learning models. Furthermore, since the data is generated, it can be tailored to specific requirements, ensuring that the labels are consistent and accurate across the entire dataset;

  • Overcoming Bias. Bias in training data can significantly impact the performance and fairness of ML models. Synthetic data generation techniques offer the opportunity to control the properties and distribution of data, thereby mitigating bias and promoting fairness in model predictions.

Disadvantages of Synthetic Data

Inaccurate reality. Artificial data can potentially misrepresent real-world occurrences due to its inability to capture the complexities and nuances present in the original dataset. Despite synthetic data capturing general patterns and correlations, it may lack the fine-grained details and subtleties inherent in real-world data. Consequently, synthetic samples might not accurately reflect real-world patterns, causing models to be poorly calibrated and unable to represent real scenarios well;

Wrong labels. Incorrect synthetic data labels can arise due to various reasons, including inherent limitations of the model used for data generation, the complexity of the task, or the ambiguity of the data itself. The model that is utilized to generate synthetic data may not accurately reproduce the diversity of the data or the relationship between features and labels. This can result in improperly labeled instances where the generated labels do not reflect the true characteristics of the data.

Overcoming Disadvantages of Synthetic Data Labeling

When synthetic data is generated by the LLM but some labels have low confidence, involving human assistance becomes necessary. In such cases, services like Toloka can come to the rescue, where such poor-quality data is sent to human experts for refinement and re-evaluation.

They can handle large volumes of data quickly by distributing tasks among a large number of experts. This scalability is particularly valuable when dealing with extensive datasets or when labels need to be verified across multiple samples.

Such a hybrid approach combines the strengths of automated labeling by the LLM with human judgment and expertise to improve the quality of the labeled data. By integrating human intervention in the synthetic data labeling process, hybrid pipelines can effectively address issues related to low-quality synthetic data, ultimately enhancing the performance of machine learning models trained on such data.

Toloka provides cost-effective solutions by leveraging a global workforce. This allows businesses to allocate resources efficiently while ensuring high-quality labeled data.

Article written by:
Toloka Team
Toloka Team

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