论文标题
全面概述和调查元学习的最新进展
A Comprehensive Overview and Survey of Recent Advances in Meta-Learning
论文作者
论文摘要
本文回顾了元学习，也称为学习对学习，它寻求快速，准确的模型适应，以通过高度自动化的AI，少数学习，自然语言处理和机器人技术中的应用程序进行了看不见的任务。与深度学习不同，可以将元学习应用于几个射击的高维数据集，并考虑进一步改善模型的概括性，从而看不见任务。深度学习集中在样本外预测和元学习问题上，模型适应了样本外预测。元学习可以不断执行自主AI的自主力。元学习可以作为原始深度学习模型的附加概括块。 Meta-Learning寻求适应机器学习模型，以看不见与受过训练的任务大不相同的任务。与代理和环境之间进行共同进化的元学习为通过从头开始训练无法解决的复杂任务提供了解决方案。元学习方法涵盖了广泛的思想和思想。我们在以下类别中简要介绍了元学习方法：黑盒元学习，基于公制的元学习，分层的元学习和贝叶斯元学习框架。最近的应用集中于将元学习与其他机器学习框架集成，以提供可行的集成问题解决方案。我们简要介绍了最近的元学习进步，并讨论了潜在的未来研究方向。
This article reviews meta-learning also known as learning-to-learn which seeks rapid and accurate model adaptation to unseen tasks with applications in highly automated AI, few-shot learning, natural language processing and robotics. Unlike deep learning, meta-learning can be applied to few-shot high-dimensional datasets and considers further improving model generalization to unseen tasks. Deep learning is focused upon in-sample prediction and meta-learning concerns model adaptation for out-of-sample prediction. Meta-learning can continually perform self-improvement to achieve highly autonomous AI. Meta-learning may serve as an additional generalization block complementary for original deep learning model. Meta-learning seeks adaptation of machine learning models to unseen tasks which are vastly different from trained tasks. Meta-learning with coevolution between agent and environment provides solutions for complex tasks unsolvable by training from scratch. Meta-learning methodology covers a wide range of great minds and thoughts. We briefly introduce meta-learning methodologies in the following categories: black-box meta-learning, metric-based meta-learning, layered meta-learning and Bayesian meta-learning framework. Recent applications concentrate upon the integration of meta-learning with other machine learning framework to provide feasible integrated problem solutions. We briefly present recent meta-learning advances and discuss potential future research directions.
