fewshot, zeroshotとは



機械学習におけるfewshot, zeroshotとは

"Few-shot learning" and "Zero-shot learning" are terms used in the field of machine learning to describe the ability of a model to learn to classify new classes with limited or no training examples.

In few-shot learning, the model is given a small number of training examples for each new class, and it must learn to generalize from these examples to classify new instances of the same class. The goal is to train the model to recognize new classes with very little supervision, as in real-world scenarios, it is often infeasible to obtain a large amount of labeled data for every possible class.

In zero-shot learning, the model is required to recognize new classes without having seen any examples of those classes during training. Instead, the model is given semantic information about the classes, such as class attributes or word embeddings, which can be used to make predictions about novel classes. The goal of zero-shot learning is to enable the model to recognize new classes that were not seen during training, by transferring knowledge from related classes.

Both few-shot and zero-shot learning are important areas of research in machine learning, as they allow models to learn from fewer examples and adapt to new, unseen classes. This can be useful in a wide range of applications, such as object recognition, text classification, and recommendation systems, where it may be difficult or impossible to obtain labeled data for every class.

「機械学習における機械学習(Few-shot learning)および機械学習(Zero-shot learning)とは、機械学習の分野において、限られた学習例あるいは全く学習例のないモデルに対して、新しいクラスの分類を学習する能力を表す用語である。