Synthetic data is computer-generated. In other words, it is data that is not collected or measured in the real world. Although it is synthetic, the data can be statistically reflective of the real world. One purpose of synthetic data is to train machine learning models.
Training ML models
There is an emphasis on data-centric approaches to training machine learning models. High volumes of data are required to train, validate and verify models. This is very resource intensive in the real world. Synthetic data provides a means of simplifying this workload. Collecting data through computer programs can cut the time-intensive processes. Since generating data at scale is costly, synthetic data can provide a cost cutting advantage.
There is great difficulty obtaining high volumes of relevant data. Collecting and processing the data is resource intensive, often leading to high costs.
By removing human error during annotation, the quality of data is improved. Processing data manually leaves room for bias or inaccuracy in the model.
Real-world data collection is complex. It takes time, vast planning and perfect execution. This is particularly true in autonomous vehicle development. Waiting for traffic or weather conditions to test is slow and uncontrollable. Furthermore, real-world data often passes through additional layers of administration, privacy and data protection. In comparison, synthetic data has a greater ease of use.
Data from the real-world depends captured at the time of collection. This means real-world datasets often are imbalanced and under-represented. Edge cases are events that do not occur frequently during data collection, meaning the dataset lacks this data. The machine learning model consequently has a bias because it does not have enough data to learn correctly. Synthetic data can supplement datasets with edge cases to avoid creating a bias.
Synthetic data must reflect the real world. This poses two challenges. Firstly, if the synthetic data is too close to reality it may need assurances of maintaining privacy. Generating realistic data that doesn’t expose private data may be difficult. However, if the data is not accurate then it won’t reflect the real world patterns. If the data is not realistic, the model is not effectively trained.
Bias is always a concern, regardless of whether the dataset is real-world or synthetically generated. For example, imagine a real-world dataset has a bias. If you use a synthetic dataset that mimics the real-world data, you’ll find the same bias is reproduced. There must be some adjustments and analysis of the real-world data to identify and account for the bias. This will ensure a representative synthetic dataset is generated.
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