深度学习--第一次跨科学领域的选定评论的元调查，它们的共性，挑战和研究影响
Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
摘要：
深度学习属于人工智能领域，机器执行通常需要某种人类智能的任务。类似于大脑的基本结构，深度学习算法由类似于生物大脑结构的人工神经网络组成。模仿人类的感官学习过程，深度学习网络被输入（感官）数据，如文本、图像、视频或声音。这些网络在不同的任务中表现优于最先进的方法，正因为如此，整个领域在过去几年中呈指数级增长。这一增长导致过去几年每年出版物超过10 000份。例如，仅搜索引擎PubMed（仅涵盖医学领域所有出版物的一个子集）在2020年第三季度就为搜索词“深度学习”提供了超过11，000个结果，其中约90%的结果来自过去三年。因此，已经不可能获得对深度学习领域的完整概述，并且在不久的将来，可能难以获得对子领域的概述。然而，有几篇关于深度学习的评论文章，它们专注于特定的科学领域或应用，例如深度学习在计算机视觉或目标检测等特定任务中的进展。以这些调查为基础，本文的目的是提供第一个高层次的、分类的元调查，对不同科学学科的深度学习的选定评论进行调查。类别（计算机视觉、语言处理、医学信息学和其他工作）是根据基础数据源（图像、语言、医学、混合）选择的。此外，我们还回顾了每个子类别的通用架构、方法、优点、缺点、评估、挑战和未来方向。
英文摘要：
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Similar to the basic structure of a brain, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image, language, medical, mixed). In addition, we review the common architectures, methods, pros, cons, evaluations, challenges and future directions for every sub-category.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
