A Survey on Deep Learning

By Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria E. Presa-Reyes, Mei-Ling Shyu, Shu‐Ching Chen, S. S. Iyengar. 2018

912 citations recorded by the source corpus.

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing

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Citations in the corpus

Split learning for health: Distributed deep learning without sharing raw patient data (2018, background): “Deep learning methods in general have found a pervasive suite of applications in biology, clinical medicine, genomics and public health as surveyed in [24, 23, 25, 29, 30, 31].”
Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges (2019, background): “Open Challenges and Trends: The interested readers can further explore using extensive survey on Deep Learning [172].”
Applications of artificial intelligence for disaster management (2020, background): “First, there may not be enough human labeled training data in time considering the increasing amount of data and the limited amount of manpower in the wake of a disaster (Pouyanfar et al. 2018).”
Machine learning and deep learning (2021, background): “Various DL architectures have emerged over time (Leijnen and van Veen 2020; Pouyanfar et al. 2019; Young et al. 2018).”
Artificial intelligence in business: State of the art and future research agenda (2020, background): “However, with hidden layers embedded in the networks, they are largely black-boxes (Pouyanfar, et al. , 2018) .”
Deep Learning for Sensor-based Human Activity Recognition (2020, background): “oaches to effectively capture distinguishable features for human activities. In recent years, deep learning has embraced conspicuous prosperity in modeling high-level abstractions from intricate data [112] in many areas such as computer vision, natural language processing, and speech processing. Aft”