Generating synthetic data

Machine learning-based systems have shown great progress and great results in real-world applications, but they have major limitations due to the quality of the data processed. In fact, the results and performances that these models return are strongly dependent on the data, on their quantity, and above all, on their quality. However, it is evident that the manual process of annotating and labeling data requires a very high level of work, which obviously increases with the amount of data generated.

Real data versus artificial data

The use of simulation systems as a method of data collection, then, becomes an effective solution that allows you to produce a large amount of data of better quality and with much less human effort. It is, in fact, possible to programmatically annotate the data that is produced and do so at a speed far superior to the real case. The precision of associating the correct annotation with data can make the difference in having...