Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two major constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such dependency and costs by reusing an obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. As a result, researchers detected Covid-19 infection on chest X-Rays with high accuracy at the beginning of the pandemic with minimal data using DTL techniques. Also, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on some adjustments for different scenarios. In this paper, we review the definition and taxonomy of deep transfer learning and well-known methods. Then we investigate the DTL approaches by reviewing recent applied DTL techniques in the past five years. Further, we review some experimental analyses of DTLs to learn the best practice for applying DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends.
Though technology assisted review in electronic discovery has been focusing on text data, the need of advanced analytics to facilitate reviewing multimedia content is on the rise. In this paper, we present several applications of deep learning in computer vision to Technology Assisted Review of image data in legal industry. These applications include image classification, image clustering, and object detection. We use transfer learning techniques to leverage established pretrained models for feature extraction and fine tuning. These applications are first of their kind in the legal industry fo…
University of Georgia（佐治亚大学）；Road and Urban Development Organization（道路与城市发展组织）
The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between dom…
In this article, the concepts of transfer and continual learning are introduced. The ensuing review reveals promising approaches for industrial deep transfer learning, utilizing methods of both classes of algorithms. In the field of computer vision, it is already state-of-the-art. In others, e.g. fault prediction, it is barely starting. However, over all fields, the abstract differentiation between continual and transfer learning is not benefitting their practical use. In contrast, both should be brought together to create robust learning algorithms fulfilling the industrial automation sector'…
Humans can learn from very few samples, demonstrating an outstanding generalization ability that learning algorithms are still far from reaching. Currently, the most successful models demand enormous amounts of well-labeled data, which are expensive and difficult to obtain, becoming one of the biggest obstacles to the use of machine learning in practice. This scenario shows the massive potential for Transfer Learning, which aims to harness previously acquired knowledge to the learning of new tasks more effectively and efficiently. In this systematic review, we apply a quantitative method to se…
Dedan Kimathi University of Technology（德丹·基马蒂理工大学）
Chest X-rays remains to be the most common imaging modality used to diagnose lung diseases. However, they necessitate the interpretation of experts (radiologists and pulmonologists), who are few. This review paper investigates the use of deep transfer learning techniques to detect COVID-19, pneumonia, and tuberculosis in chest X-ray (CXR) images. It provides an overview of current state-of-the-art CXR image classification techniques and discusses the challenges and opportunities in applying transfer learning to this domain. The paper provides a thorough examination of recent research studies t…
Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人形与机器人研究所）；KTH Royal Institute of Technology（KTH皇家理工学院）；University of Ljubljana（卢布尔雅那大学）
A Simple Review of EEG Foundation Models: Datasets, Advancements and Future Perspectives
The College of Computer Science, Zhejiang University, Hangzhou, China.（浙江大学计算机科学学院）；The MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.（脑科学与脑机接口前沿科学中心）；The Nanhu Brain-Computer Interface Institute, Hangzhou, China.（南湖脑机接口研究院）；The Qiushi Academy for Advanced Studies, Hangzhou, China.（启石先进研究院）
Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review
Georgia Institute of Technology, College of Computing（佐治亚理工学院计算机学院）；Harvard University, School of Engineering and Applied Sciences（哈佛大学工程与应用科学学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Columbia University, The Fu Foundation School of Engineering and Applied Science（哥伦比亚大学傅以文工程与应用科学学院）
Multimodal Federated Learning in Healthcare: a Review
West Virginia University（西弗吉尼亚大学）；Tribhuvan University（特里布万大学）；University at Albany（奥尔巴尼大学）；University of Pittsburgh（匹兹堡大学）；University of Aberdeen（阿伯丁大学）
Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
Shanghai Jiao Tong University（上海交通大学）；Georgia Institute of Technology（佐治亚理工学院）；Shanghai AI Laboratory（上海人工智能实验室）；University of Georgia（佐治亚大学）；Oxford University（牛津大学）
The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) …
A Systematic Review of Conformal Inference Procedures for Treatment Effect Estimation: Methods and Challenges
Engineering Mathematics and Computing Lab Heidelberg University（海德堡大学工程数学与计算实验室）；Heidelberg University（海德堡大学）；Heidelberg Institute for Theoretical Studies, Data Mining and Uncertainty Quantification Group（海德堡理论研究所数据挖掘与不确定性量化组）；Medical Faculty Mannheim of Heidelberg University, Data Analysis and Modeling in Medicine（海德堡大学曼海姆医学系医学数据分析与建模）
Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey
Lassonde School of Engineering, York University（约克大学拉松德工程学院）；Huazhong University of Science and Technology（华中科技大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）
Text style transfer is a hot issue in recent natural language processing,which mainly studies the text to adapt to different specific situations, audiences and purposes by making some changes. The style of the text usually includes many aspects such as morphology, grammar, emotion, complexity, fluency, tense, tone and so on. In the traditional text style transfer model, the text style is generally relied on by experts knowledge and hand-designed rules, but with the application of deep learning in the field of natural language processing, the text style transfer method based on deep learning St…
