Researchers Demonstrate Less-than-One Shot Machine Learning

We're accustomed to thinking that bigger is better in machine learning. If 10 samples are good, then 100 samples must be even better. However, researchers from the University of Waterloo recently demonstrated the feasibility of "less than one-shot" learning, or a model that can learn to identify something, even if it's never seen an example of it. In their September paper, titled "'Less Than One'-Shot Learning: Learning N Classes From M N Samples," researchers Ilia Sucholutsky and Matthias Schonlau explain how they created a machine learning model that can learn to classify something when trained with less than one example per class. For example, consider an alien zoologist who lands on earth and is instructed to capture a unicorn.