Lean and Augmented Data Learning
The biggest challenge in machine learning (deep learning, in particular), is the availability of large volumes of labeled data to train the system.
Augmented Data Learning Abilities
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.