Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact

• URL: http://arxiv.org/abs/2011.08184v2
• Date: Wed, 17 Nov 2021 12:40:20 GMT
• Title: Deep Learning -- A first Meta-Survey of selected Reviews across Scientific Disciplines, their Commonalities, Challenges and Research Impact
• Authors: Jan Egger, Antonio Pepe, Christina Gsaxner, Yuan Jin, Jianning Li, Roman Kern
• Abstract summary: This contribution provides a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines. Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. 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'
• Score: 4.505014335388935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• An inclusive review on deep learning techniques and their scope in handwriting recognition [4.318047857743103]
  Deep learning expresses a category of machine learning algorithms that have the capability to combine raw inputs into intermediate features layers. Deep learning has particularly witnessed for a great achievement of human level performance across a number of domains in computer vision and pattern recognition. This paper presents a survey on the existing studies of deep learning in handwriting recognition field.
  arXiv  Detail & Related papers  (2024-04-10T06:30:33Z)
• Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review [1.25828876338076]
  Brain-inspired computing is an important intersection between multimodal technology and biomedical field. This paper presents a review of the brain-inspired computing models based on machine learning (ML) and deep learning (DL) Despite significant advances in brain-inspired computational models, challenges remain to fully exploit their capabilities.
  arXiv  Detail & Related papers  (2023-12-12T12:26:37Z)
• Unveiling the frontiers of deep learning: innovations shaping diverse domains [6.951472438774211]
  Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. This paper extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges.
  arXiv  Detail & Related papers  (2023-09-06T04:50:39Z)
• Neural Architecture Search: Insights from 1000 Papers [50.27255667347091]
  We provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques. We discuss resources such as benchmarks, best practices, other surveys, and open-source libraries.
  arXiv  Detail & Related papers  (2023-01-20T18:47:24Z)
• Deep Learning to See: Towards New Foundations of Computer Vision [88.69805848302266]
  This book criticizes the supposed scientific progress in the field of computer vision. It proposes the investigation of vision within the framework of information-based laws of nature.
  arXiv  Detail & Related papers  (2022-06-30T15:20:36Z)
• Neural Architecture Search for Dense Prediction Tasks in Computer Vision [74.9839082859151]
  Deep learning has led to a rising demand for neural network architecture engineering. neural architecture search (NAS) aims at automatically designing neural network architectures in a data-driven manner rather than manually. NAS has become applicable to a much wider range of problems in computer vision.
  arXiv  Detail & Related papers  (2022-02-15T08:06:50Z)
• Neural Fields in Visual Computing and Beyond [54.950885364735804]
  Recent advances in machine learning have created increasing interest in solving visual computing problems using coordinate-based neural networks. neural fields have seen successful application in the synthesis of 3D shapes and image, animation of human bodies, 3D reconstruction, and pose estimation. This report provides context, mathematical grounding, and an extensive review of literature on neural fields.
  arXiv  Detail & Related papers  (2021-11-22T18:57:51Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI [2.0178765779788495]
  Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks.
  arXiv  Detail & Related papers  (2020-05-19T18:10:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.