Egger Jan, Pepe Antonio, Gsaxner Christina, Jin Yuan, Li Jianning, Kern Roman
Institute of Computer Graphics and Vision, Faculty of Computer Science and Biomedical Engineering, Graz University of Technology, Graz, Austria.
Computer Algorithms for Medicine Laboratory, Graz, Austria.
PeerJ Comput Sci. 2021 Nov 17;7:e773. doi: 10.7717/peerj-cs.773. eCollection 2021.
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, 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 and outline the research impact that they already have during a short period of time. 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.
深度学习属于人工智能领域，在该领域中，机器执行通常需要某种人类智能才能完成的任务。深度学习试图通过从人类大脑的学习过程中汲取灵感来实现这一目标。类似于大脑的基本结构，它由（数十亿个）神经元及其之间的连接组成，深度学习算法由人工神经网络组成，该网络类似于生物大脑结构。深度学习网络模仿人类通过感官进行学习的过程，被输入（感官）数据，如文本、图像、视频或声音。这些网络在不同任务中优于现有最先进的方法，正因为如此，在过去几年中，整个领域呈现出指数级增长。这种增长导致近年来每年有超过10000篇相关出版物。例如，仅涵盖医学领域所有出版物子集的搜索引擎PubMed，在2020年第三季度，仅搜索词“深度学习”就已经提供了超过11000条结果，其中约90%的结果来自过去三年。因此，已经不可能全面了解深度学习领域，在不久的将来，可能很难全面了解某个子领域。然而，有几篇关于深度学习的综述文章，它们专注于特定的科学领域或应用，例如计算机视觉中的深度学习进展或目标检测等特定任务。以这些综述为基础，本论文的目的是对不同科学学科中关于深度学习的精选综述进行首次高层次、分类的元综述，并概述它们在短时间内已经产生的研究影响。这些类别（计算机视觉、语言处理、医学信息学和其他作品）是根据基础数据源（图像、语言、医学、混合）来选择的。此外，我们还将回顾每个子类别的常见架构、方法、优点、缺点、评估、挑战和未来方向。
