Synthetic Data Can Help Enhance Performance In Machine Learning
Added: (Thu Nov 10 2022)
Pressbox (Press Release) -
In some circumstances, models trained on synthetic data can outperform other models in terms of accuracy, which may alleviate some of the ethical, copyright, and privacy concerns associated with utilising real data.
Teaching a machine to recognise human actions has many potential applications, such as automatically detecting construction workers who fall or allowing a smart home robot to interpret a user's gestures.
To accomplish this, researchers train machine-learning models on massive datasets of video clips of humans performing actions. However, not only is it costly and time-consuming to carry out Data Collection for Artificial Intelligence and label millions or billions of videos, but the clips frequently contain sensitive information such as people's faces or licence plate numbers. Using these videos may also constitute a violation of copyright or data protection laws. And this is assuming that the video data is publicly available to begin with — many datasets are owned by companies and are not free to use.
As a result, scientists are turning to synthetic datasets. These are created by a computer that uses 3D models of scenes, objects, and humans to generate a large number of different clips of specific actions — without the potential copyright issues or ethical concerns that come with real Data Collection Services for AI.
Is synthetic data, however, as "good" as real data? How does a model trained with this data perform when asked to classify real-world human actions? Following extensive research, it was discovered that synthetically trained models outperformed models trained on real data for videos with fewer background objects.
This research could help researchers use synthetic datasets to improve model accuracy on real-world tasks. It could also assist scientists in determining which machine-learning applications are best suited for training with synthetic data, thereby minimising some of the ethical, privacy, and copyright concerns associated with using real datasets.
The ultimate goal is to use synthetic data pre training instead of real data pre training. There is a cost associated with creating an action in synthetic data, but once completed, you can generate an infinite number of images or videos by changing the pose, lighting, and so on. That's the appeal of synthetic data.
Despite the fact that businesses process hundreds of thousands of data points, they still face data access issues. Long access procedures for rare disease data collection may be encountered by healthcare organisations. Accessing data about fraudulent transactions may be difficult for a financial institution.
By significantly reducing the time required to access data, synthetic data can help to solve the access to data collection for artificial intelligence problems. In contrast to sensitive datasets, properly anonymized synthetic data does not require the lengthy access request process.
Data science teams frequently spend time cleaning data before using it to fuel ML algorithms. This time-consuming process is critical to the AI project's success. Poor quality or misleading data will have a negative impact on Machine Learning results.
The generation of synthetic data can aid in the automation of the data cleaning process. For example, differentially-private synthetic data suppresses outliers, which aids in bias reduction and training data quality improvement.
As a result, properly generated synthetic data can improve the quality of the original data and help your AI project succeed. Synthetic data is also ready to use, so there is no need to clean or format it.
It takes months to go through compliance verification processes in order to open up real-world data or obtain secondary consent to use it for ML Models. In many cases, either consent is not obtained or the de-identified data quality is insufficient to support a successful ML application.
Creating synthetic data with the appropriate privacy guarantees can help to speed up the compliance process. Because privacy-preserving synthetic data does not contain real-world data or sensitive personal data, the legal constraints surrounding data processing are much lighter. For example, you do not need to obtain secondary consent to use anonymized synthetic data in a new machine learning project.
Using synthetic data also protects your customers' privacy, exposing them to less risk. As a result, you can experiment on a synthetic dataset, test different machine learning models, see what works and what doesn't, and process the data without the fear of violating privacy laws.
Finally, using synthetic data opens up new avenues for collaboration and establishes a new foundation for the success of the ML project. You can work with a third party, for example, to use synthetic data in a Proof Of Concept (POC) and test it before implementing it on a large scale.