Adopt synthetic visual data to improve AI models

Businesses are succeeding with synthetic visual data

Businesses are exploring or applying synthetic visual data to make functions ranging from designing products to providing healthcare more efficient. Here are some examples of how synthetic visual data can make the development of products and services more efficient:

Waymo uses a simulated environment called Carcraft to train its self-driving AI. This involves generating synthetic visual data of various road conditions, traffic patterns, and pedestrian behaviors.
Nike has explored using synthetic data to create virtual try-on experiences and generate images of new shoe designs on diverse models.
The Mayo Clinic has examined using synthetic data to train AI models for medical imaging analysis, which enables more accurate diagnoses and personalized treatment plans.

According to the Mayo Clinic, synthetic visual data may be able to generate viable images of people from underrepresented groups. This will help ensure that diagnostic AI models are more accurate and inclusive because they represent different segments of the population more completely.

As early adopters refine their use cases, they are potentially making their businesses more adaptable and resilient as the technology evolves.

Synthetic visual data provides several benefits

Synthetic visual data can help your business advance your AI capabilities, reduce training costs, and navigate ethical and regulatory landscapes more effectively.

Synthetic visual data empowers your business with flexibility and control, which drives operational efficiency and enables you to tackle complex, real-world challenges with greater confidence and precision.

Synthetic visual data can be scaled more cost effectively

Generating visual synthetic data allows businesses to create large datasets at a fraction of the cost and time needed to gather real-world data. This scalability is important for fields such as autonomous driving, where vast amounts of labeled data are required to train models effectively.

Synthetic visual data can simulate rare and complex scenarios, such as unusual weather patterns or uncommon traffic incidents, without the financial and logistical barriers of collecting real-world examples.

Synthetic visual data is more complete

As with all synthetic data, synthetic visual data can help address the gaps where real-world data might fall short. As a result, AI models can generalize effectively across different use cases, and your data can reflect a wide range oof scenarios that may not be easily accessible otherwise.

For instance, medical imaging datasets can be adjusted to represent different anatomical variations, helping AI models generalize better across patient demographics.

This flexibility also supports industries like e-commerce, where synthetic data can generate product images across various settings and lighting conditions, enabling you to personalize content for diverse audiences.

Synthetic visual data helps businesses protect user privacy and to be compliant

By creating visual data without involving real individuals, synthetic data allows companies to train AI models without the risk of privacy violations or legal issues associated with using personal information.

This is a major benefit in industries bound by strict privacy regulations, such as healthcare or finance, where real data might be scarce or legally challenging to obtain. Companies can develop AI models using realistic but anonymized data while maintaining compliance with privacy standards like GDPR, HIPAA, and CCPA. This approach maintains a balance between data utility and ethical responsibility.

Synthetic visual data is especially useful for safety-critical applications

Synthetic visual data offers control over the conditions under which images are generated, which allows your business to test AI models under specific, controlled scenarios.

This control is particularly valuable in safety-critical applications, such as autonomous vehicles, where the ability to simulate different lighting, weather, or road conditions enhances the effectiveness of the model.

For example, synthetic data can be used to create images representing foggy or snowy roads so that the AI model performs well in less-than-ideal visibility conditions, which might be dangerous or rare to replicate in real life.

Synthetic data helps your business overcome the problem of data imbalances

Real-world datasets often suffer from biases or imbalances, which can hinder the performance of AI models, particularly in applications requiring inclusivity across diverse groups. Synthetic visual data allows companies to proactively address these issues by generating balanced datasets that represent a wider array of environments, conditions, and demographics.

This is particularly beneficial in fields like facial recognition, where synthetic data can help balance representations across various age, gender, and ethnic groups, leading to more accurate and fair outcomes in AI models.

As more companies adopt synthetic data, they can experiment with innovative AI applications in a safe and controlled environment, further pushing the boundaries of what AI can accomplish.

Synthetic visual data presents challenges, too

While synthetic visual data offers several advantages, some challenges still need to be addressed to maintain its reliability and effectiveness. They include difficulty in generating realistic human features, the potential for bias, and the complexity of validation. As synthetic visual data is still a relatively new technology, addressing these challenges is important for its widespread adoption and success.

Synthetic visual data struggles with photorealism

Synthetic visual data often falls short when it comes to rendering complex human features, such as faces, hands, and expressions. These intricate details are vital for tasks like facial recognition or human-object interaction.

The lack of realism can limit the effectiveness of AI models trained solely on synthetic data, particularly when they are deployed in real-world environments where accuracy is essential. For example, models might struggle with understanding subtle variations in human expressions or gestures, diminishing their performance in applications like social robots or digital assistants.

Synthetic visual data can introduce or magnify bias

Synthetic data generation can inadvertently introduce or even magnify biases, leading to skewed model outcomes. This happens when the data generation process relies too heavily on certain assumptions or parameters that do not fully capture the diversity of the real world.

For example, efforts to make data generation inclusive may result in historically inaccurate depictions, as seen when models attempt to recreate cultural or historical scenes. This issue can distort how models interpret real-world data, making them less reliable when tasked with understanding diverse populations or scenarios.

Validating synthetic data also presents a formidable challenge

Unlike real-world datasets, which inherently represent authentic scenarios, synthetic visual data requires rigorous validation so that it accurately mirrors the conditions it aims to simulate. This is especially important in applications where precision matters, such as generating visuals with specific camera settings or lighting conditions.

For instance, even when an AI model is able to generate an image of a dog in a backyard with defined camera specifications, it can be difficult to confirm that the image truly matches those settings. Without proper validation, synthetic data risks becoming an unreliable foundation for training AI models.

Generating synthetic visual data can be complicated

The generation of synthetic visual data often involves complex simulation techniques, such as physics-based rendering. This complexity can make the process computationally expensive and time-consuming.

Simulating realistic lighting, textures, and 3D environments is essential for applications like autonomous driving or virtual reality, where visual fidelity is critical. These advanced techniques require significant resources, adding to the costs and barriers of developing synthetic data.

How to succeed with synthetic visual data

To overcome these challenges, your business should human oversight with technological innovation. Balancing human oversight with some proven approaches such as ensemble techniques makes it possible for you to capture the benefits of synthetic visual data and minimize the challenges.

Keep humans in the loop with synthetic visual data

One approach: incorporating human feedback directly into the data generation process through methods like reinforcement learning with human feedback (RLHF). This iterative technique enables AI models to adjust and refine their outputs based on human evaluations, which aligns the generated data more closely with real-world expectations.

For example, when AI-generated images are reviewed by human experts, their feedback can guide the model in making adjustments that improve the realism and relevance of the data.

Hybrid solutions that integrate physics-based rendering with generative models like generative adversarial networks (GANs) have also shown promise. The models use real-world datasets to guide the creation of synthetic visuals, which allows for a more nuanced representation of complex scenes.

Combining the strengths of generative methods and physical simulations allows these models to produce images that better capture the subtleties of real-world lighting, shadows, and textures, making them more suitable for practical applications.

Deploy ensemble techniques

This approach of using ensemble techniques involves deploying multiple specialized models, each focusing on generating a particular type of image, such as pets, human figures, or urban scenes.

Combining the outputs of these specialized models creates more coherent and realistic synthetic scenes. For example, while one model generates a human figure, another might focus on simulating the environment around it, such as a park or cityscape, resulting in a more natural and integrated final image.

Tap into the power of crowdsourcing

You can improve the realism and variety of your synthetic visuals by gathering diverse real-world datasets through a crowdsourced model. This curated data strengthens the alignment of synthetic data with real-world conditions, which reduces the risk of bias and makes the generated images more applicable.

Crowdsourcing real-world data allows for better calibration of synthetic models, helping to fill gaps in representation and ensure a more balanced training dataset.

Realize the potential of synthetic visual data

While synthetic visual data holds immense potential, it is important to remember that it is not a magic solution. The effectiveness of synthetic visual data depends heavily on the quality of the data and the methods used to generate it. You can harness the power of synthetic visual data to transform AI  by understanding the challenges and embracing innovative solutions.

As synthetic visual data technology evolves, we can expect to see even more impressive advancements in AI capabilities across industries. So, it’s important to start learning and improving now so that your business keeps pace with change.

A frontier AI data foundry platform can help you apply synthetic visual data by drawing on best practices and a deep understanding of the technology and pitfalls using it. For example, Centific SynthVision is a synthetic data platform that generates images and videos for varied client needs, using computer vision, camera control, and AI technologies.

Combining precision tools with an extensive annotation workforce, it produces visuals of people and pets in different environments and lighting conditions, supporting applications like autonomous driving simulations and travel itineraries. Its quality control framework, including automated QA and responsible AI assessments, aligns realistic outputs with client requirements.

Learn more about Centific’s frontier AI data foundry platform.