论文标题
少量学习域中深度学习体系结构的概述
An Overview of Deep Learning Architectures in Few-Shot Learning Domain
论文作者
论文摘要
自2012年以来，深度学习彻底改变了人工智能，并在不同领域取得了最新的成果，从图像分类到言语产生。尽管它具有许多潜力，但我们当前的体系结构伴随着大量数据的先决条件。几乎没有射击的学习（也称为单一学习）是机器学习的子场，旨在创建可以使用更少的数据来学习所需目标的模型，类似于人类的学习方式。在本文中，我们回顾了一些众所周知的基于深度学习的方法，用于几次学习。我们讨论了改进基于学习的深度学习体系结构的最新成就，挑战和可能性。我们对本文的目的是三倍：（i）简要介绍了深度学习的架构，用于几次学习，并用指示核心参考。 （ii）指出从数据准备到模型培训的深度学习如何应用于低数据。而且，（iii）通过指出一些有用的资源和开源代码来实验有兴趣的人，也许可以为几乎没有射击学习的领域提供一个起点。我们的代码可在GitHub：https：//github.com/shruti-jadon/hands-on-on-one-shot-learning上找到。
Since 2012, Deep learning has revolutionized Artificial Intelligence and has achieved state-of-the-art outcomes in different domains, ranging from Image Classification to Speech Generation. Though it has many potentials, our current architectures come with the pre-requisite of large amounts of data. Few-Shot Learning (also known as one-shot learning) is a sub-field of machine learning that aims to create such models that can learn the desired objective with less data, similar to how humans learn. In this paper, we have reviewed some of the well-known deep learning-based approaches towards few-shot learning. We have discussed the recent achievements, challenges, and possibilities of improvement of few-shot learning based deep learning architectures. Our aim for this paper is threefold: (i) Give a brief introduction to deep learning architectures for few-shot learning with pointers to core references. (ii) Indicate how deep learning has been applied to the low-data regime, from data preparation to model training. and, (iii) Provide a starting point for people interested in experimenting and perhaps contributing to the field of few-shot learning by pointing out some useful resources and open-source code. Our code is available at Github: https://github.com/shruti-jadon/Hands-on-One-Shot-Learning.
