Improving AI using transfer and few-shot learning techniques

In the field of few-shot learning, the aim is to make use of pre-existing knowledge gained from pre-trained models in order to deal with unknown categories. Rather than starting the process of training a model from scratch with a large amount of data, few-shot learning focuses on providing the model with a smaller collection of data, called the "support set," which comprises a limited number of examples from each new category.