It has the ability to learn from a different type of data and require less kind data.
Data Augmentation Techniques
Using these techniques, we can address a wider variety of problems, especially those with less historical data. Expect to see more variations of lean and augmented data, as well as different types of learning applied to a broad range of business problems. Two broad techniques can help address this:
(1) Synthesizing new data
(2) Transferring a model trained for one task or domain to another.
Techniques, such as transfer learning (transferring the insights learned from one task/domain to another) or one-shot learning (transfer learning taken to the extreme with learning occurring with just one or no relevant examples) — making them “lean data” learning techniques. Similarly, synthesizing new data through simulations or interpolations helps to obtain more data, thereby augmenting existing data to improve learning.
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