Data augmentation is an important term in the fields of artificial intelligence, big data, smart data and automation. It refers to methods used to artificially enlarge or expand existing data sets in order to create a better basis for the development and training of algorithms.
Suppose a company wants to develop an artificial intelligence that automatically recognises photos of products. To do this, the AI needs many different images of each product. However, there are often not enough photos available. This is where data augmentation comes into play: computer programs are used to create new variants from the existing images - for example, by changing the colours, mirroring the image or rotating it slightly. This creates a lot of additional data quickly and without much effort.
The advantage of data augmentation is that algorithms become more robust and accurate because they can be trained with a greater variety of examples. This saves time and money because less real data needs to be collected and significantly improves the quality of artificial intelligence results.
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