A data-centric review of deep transfer learning with applications to text data

Bashath, Samar; Perera, Nadeesha; Tripathi, Shailesh; Manjang, Kalifa; Dehmer, Matthias; Emmert‐Streib, Frank

doi:10.1016/j.ins.2021.11.061

Cited by 47 publications

(20 citation statements)

References 65 publications

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“…It has emerged as a popular and promising area of machine learning due to its wide range of application possibilities, especially in solving real-world problems [ 78 , 79 , 80 , 81 ] in a cheaper and more reliable method. The sphere of use of transfer learning is not few, coupled with its record of high results [ 82 , 83 ].…”

Section: Methodsmentioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.

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Section: Methodsmentioning

confidence: 99%

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Shekonya

Xia

Kyslytysna

et al. 2022

Plants

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“…To learn better representations of data instances and perform well in a task, deep learning models such as transformer-based models require large enough training data [27], [28], and training and testing data from the same underlying distribution [28], [29]. In real-world scenarios, new applications of deep learning models, such as ours, suffer from limited or lack of training data.…”

Section: Background and Related Workmentioning

confidence: 99%

cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning

Mahbub

Begoli

Martins³

et al. 2023

IEEE Access

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This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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“…Comprehensive reviews of TL can be found in Bashath et al (2022), Pan and Yang (2009), Weiss et al (2016), and Zhuang et al (2020).…”

Section: Transfer Learningmentioning

confidence: 99%

Taxonomy of machine learning paradigms: A data‐centric perspective

Emmert‐Streib

Dehmer

2022

WIREs Data Min & Knowl

Self Cite

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Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Based on these, thousands of different methods have been developed during the last seven decades used in nearly all application domains. However, recently, other learning paradigms are gaining momentum which complement and extend the above learning paradigms significantly. These are multi‐label learning (MLL), semi‐supervised learning (SSL), one‐class classification (OCC), positive‐unlabeled learning (PUL), transfer learning (TL), multi‐task learning (MTL), and one‐shot learning (OSL). The purpose of this article is a systematic discussion of these modern learning paradigms and their connection to the traditional ones. We discuss each of the learning paradigms formally by defining key constituents and paying particular attention to the data requirements for allowing an easy connection to applications. That means, we assume a data‐driven perspective. This perspective will also allow a systematic identification of relations between the individual learning paradigms in the form of a learning‐paradigm graph (LP‐graph). Overall, the LP‐graph establishes a taxonomy among 10 different learning paradigms. This article is categorized under: Technologies > Machine Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining

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A data-centric review of deep transfer learning with applications to text data

Cited by 47 publications

References 65 publications

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

Tomato Leaf Disease Recognition on Leaf Images Based on Fine-Tuned Residual Neural Networks

cpgQA: A Benchmark Dataset for Machine Reading Comprehension Tasks on Clinical Practice Guidelines and a Case Study Using Transfer Learning

Taxonomy of machine learning paradigms: A data‐centric perspective

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