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Zero-shot learning is a relatively new technique in machine learning (ML) that’s already having a major impact. With this method, ML systems such as neural networks require zero or very few “shots” in order to arrive at the “correct” answer. It has primarily gained ground in fields such as image classification and object detection and for Natural Language Processing (NLP), addressing the twin challenges in ML of having “too much data” as well as “not enough data”.
But the potential for zero-shot learning extends well beyond the static visual or linguistic fields. Many other use cases are emerging with applications across almost every industry and field, helping to spur re-imagination of the way humans approach that most human of activities — conversation.
How does zero-shot learning work?
Zero-shot learning allows models to learn to recognize things they haven’t been introduced to before. Rather than the traditional method of sourcing and labelling huge data sets — which are then used to train supervised models — zero-shot learning appears little short of magical. The model does not need to be shown what something is in order to learn to recognize it. Whether you’re training it to identify a cat or a carcinoma, the model uses different types of auxiliary information associated with the data to interpret and deduce.
Assimilating zero-shot learning with ML networks holds many advantages for developers across a wide range of fields. First, it dramatically speeds up ML projects because it cuts down on the most labor-intensive phases, data prep and the creation of custom, supervised models.
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