Archives for zero shot learning
Flamingo’s ability to handle interwoven text and visuals makes it a natural fit for in-context few-shot learning, similar to GPT-3, which also used few-shot text prompting.
Generating images from text method works by combining the observed and unobserved categories of text descriptions through some types of auxiliary information, which encodes observable distinguishing properties of objects.
A study in 2019 titled ‘Meta-Transfer Learning for Few Shot Learning’ addressed the challenges that few-shot settings faced.
To avoid data labelling, we can utilise zero-shot learning that aims to perform modelling using less amount of labelled data. When this learning comes to text classification, we call the whole process zero-shot text classification.