Contrastive learning is a term used in the fields of artificial intelligence, big data, smart data and digital transformation. It describes a special method of how computers learn to better understand and differentiate between data.
Imagine you want to teach software to recognise the difference between dogs and cats. In contrastive learning, you show the computer lots of pictures: Some in which the animals look similar and others in which they look very different. The software then learns which characteristics are typical of dogs and which are typical of cats by closely analysing the differences ("contrasts") between the images.
Contrastive learning is particularly helpful when there is little information available or it is difficult to label everything by hand. Modern artificial intelligence can use this technology to find patterns, for example to detect unusual transactions in large amounts of data in the financial sector (e.g. at banks) or to monitor machines in a factory. This saves time, makes processes more secure and helps to make smarter decisions.
In short: Contrastive learning is an innovative learning method for computers to better recognise differences in data and gain practical insights from them.
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