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Journal articles on the topic 'Transfer of Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

Alla, Sri Sai Meghana, and Kavitha Athota. "Brain Tumor Detection Using Transfer Learning in Deep Learning." Indian Journal Of Science And Technology 15, no. 40 (2022): 2093–102. http://dx.doi.org/10.17485/ijst/v15i40.1307.

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Würschinger, Hubert, Matthias Mühlbauer, and Nico Hanenkamp. "Transfer Learning für visuelle Kontrollaufgaben/Potentials of Transfer Learning." wt Werkstattstechnik online 110, no. 04 (2020): 264–69. http://dx.doi.org/10.37544/1436-4980-2020-04-98.

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In der industriellen Praxis wird eine Vielzahl von Prozess- und Qualitätskontrollaufgaben visuell von Mitarbeitern oder mithilfe von Kamerasystemen durchgeführt. Durch den Einsatz Künstlicher Intelligenz (KI) lässt sich die Programmierung und damit die Implementierung von Kamerasystemen effizienter gestalten. Im Bereich der Bildanalyse können dabei vortrainierte Künstliche Neuronale Netze verwendet werden. Das Anwenden dieser Netze auf neue Aufgaben wird dabei Transfer Learning genannt. &nbsp; In industrial practice, a large number of process and quality control tasks are performed visually by employees or with the aid of camera systems. By using artificial intelligence, the programming effort and thus the implementation of camera systems can be made more efficient. Pre-trained ^neural networks can be used for image analysis. The application of these networks to new tasks is called transfer learning.

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Xu, Mingle, Sook Yoon, Jaesu Lee, and Dong Sun Park. "Unsupervised Transfer Learning for Plant Anomaly Recognition." Korean Institute of Smart Media 11, no. 4 (2022): 30–37. http://dx.doi.org/10.30693/smj.2022.11.4.30.

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Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

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Tejaswini, Dubasi, and Uma Rani Vanamala. "Security System based on Transfer Learning Model." International Journal of Science and Research (IJSR) 12, no. 10 (2023): 1144–49. http://dx.doi.org/10.21275/sr231013184540.

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Vaishnavi, J., and V. Narmatha. "Novel Transfer Learning Attitude for Automatic Video Captioning Using Deep Learning Models." Indian Journal Of Science And Technology 15, no. 43 (2022): 2325–35. http://dx.doi.org/10.17485/ijst/v15i43.1846.

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Cao, Bin, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. "Adaptive Transfer Learning." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 407–12. http://dx.doi.org/10.1609/aaai.v24i1.7682.

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Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a "safe transfer" of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.

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Yu, Zhengxu, Dong Shen, Zhongming Jin, Jianqiang Huang, Deng Cai, and Xian-Sheng Hua. "Progressive Transfer Learning." IEEE Transactions on Image Processing 31 (2022): 1340–48. http://dx.doi.org/10.1109/tip.2022.3141258.

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8

Renta-Davids, Ana-Inés, José-Miguel Jiménez-González, Manel Fandos-Garrido, and Ángel-Pío González-Soto. "Transfer of learning." European Journal of Training and Development 38, no. 8 (2014): 728–44. http://dx.doi.org/10.1108/ejtd-03-2014-0026.

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Purpose – This paper aims to analyse transfer of learning to workplace regarding to job-related training courses. Training courses analysed in this study are offered under the professional training for employment framework in Spain. Design/methodology/approach – During the training courses, trainees completed a self-reported survey of reasons for participation (time 1 data collection, N = 447). Two months after training, a second survey was sent to the trainees by email (time 2 data collection, N = 158). Factor analysis, correlations and multiple hierarchical regressions were performed. Findings – The results of this study demonstrate the importance of training relevance and training effectiveness in transfer of training. Results indicated that relevance, the extent training courses were related to participant’s workplace activities and professional development, positively influences transfer of training. Effectiveness, training features which facilitated participants to acquire knowledge and skills, also has a significantly positive influence in transfer of training. Motivation to participate and learning-conducive workplace features also have a positive influence in transfer of training. Originality/value – This study contributes to the understanding of transfer of learning in work-related training programmes by analysing the factors that influence transfer of learning back to the workplace. The study has practical implication for training designers and education providers to enhance work-related training in the context of the Professional Training for Employment Subsystem in Spain.

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9

Tetzlaff, Linda. "Transfer of learning." ACM SIGCHI Bulletin 17, SI (1986): 205–10. http://dx.doi.org/10.1145/30851.275631.

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10

Koçer, Barış, and Ahmet Arslan. "Genetic transfer learning." Expert Systems with Applications 37, no. 10 (2010): 6997–7002. http://dx.doi.org/10.1016/j.eswa.2010.03.019.

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Tetzlaff, Linda. "Transfer of learning." ACM SIGCHI Bulletin 18, no. 4 (1987): 205–10. http://dx.doi.org/10.1145/1165387.275631.

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12

Zhao, Peilin, Steven C. H. Hoi, Jialei Wang, and Bin Li. "Online Transfer Learning." Artificial Intelligence 216 (November 2014): 76–102. http://dx.doi.org/10.1016/j.artint.2014.06.003.

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13

SATO, Hirokazu, Ryoji OTSU, Yonghoon JI, Hiromitsu FUJII, and Hitoshi KONO. "Automatic Transfer Rate Estimation for Transfer Learning in Reinforcement Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A2—J03. http://dx.doi.org/10.1299/jsmermd.2020.2a2-j03.

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Gardie, Birhanu, Smegnew Asemie, Kasahun Azezew, and Zemedkun Solomon. "Potato Plant Leaf Diseases Identification Using Transfer Learning." Indian Journal of Science and Technology 15, no. 4 (2022): 158–65. http://dx.doi.org/10.17485/ijst/v15i4.1235.

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15

Chaithanya, A. Sainath, and M. Rachana. "Identification of Diseased Papaya Leaf through Transfer Learning." Indian Journal Of Science And Technology 16, no. 48 (2023): 4676–87. http://dx.doi.org/10.17485/ijst/v16i48.2690.

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Krishnaveni, Ms T., and M. Saral. "Lung Cancer Prediction Using CNN And Transfer Learning." International Journal of Research Publication and Reviews 6, no. 3 (2025): 4869–74. https://doi.org/10.55248/gengpi.6.0325.1228.

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Yamini, C. Tejaswani, M., M. Yamini, Y. Deepthi, and G. M. ANAND REDDY. "Enhancing Digital image forgery detection using transfer learning." International Journal of Research Publication and Reviews 6, no. 5 (2025): 11600–11602. https://doi.org/10.55248/gengpi.6.0525.18107.

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Velada, Raquel, António Caetano, Reid Bates, and Ed Holton. "Learning transfer – validation of the learning transfer system inventory in Portugal." Journal of European Industrial Training 33, no. 7 (2009): 635–56. http://dx.doi.org/10.1108/03090590910985390.

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S, Sunmathi, and Dr Shanthi AL. "Transfer Learning Based Paddy Leaf Disease Detection Using Deep Learning & Fertilizer Recommentation." International Journal of Research Publication and Reviews 6, no. 3 (2025): 1739–43. https://doi.org/10.55248/gengpi.6.0325.1157.

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Gui, Lin, Ruifeng Xu, Qin Lu, Jiachen Du, and Yu Zhou. "Negative transfer detection in transductive transfer learning." International Journal of Machine Learning and Cybernetics 9, no. 2 (2017): 185–97. http://dx.doi.org/10.1007/s13042-016-0634-8.

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21

Mounika, Yasaswi, Vandana, and Joshna Sai. "Melanoma classification using deep transfer learning." International Journal of Data Informatics and Intelligent computing (IJDIIC) 1, no. 1 (2022): 11–20. https://doi.org/10.5281/zenodo.7101199.

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Melanoma is the most lethal type of skin cancer, despite the fact that individuals who are discovered early have a decent chance of recovering. A few creators have looked at various strategies to deal with programmed location and conclusion using design recognition and AI technology. Anticipating an infection so that it does not spread It is often helpful when doctors can diagnose an illness early on and spread throughout the body. Early disease detection is quite difficult due to the small number of screening populations. Whatever the case, it will take time to determine if it is harmless or hazardous. Assume the afflicted person sees a critical specialist for analysis, unaware that the critical specialist's knowledge has resulted in a cancerous development. This is where AI and deep learning technologies become a vital component of an effective mechanised determination framework, which might help doctors forecast infections much more swiftly and even ordinary people analyse a sickness. Our study endeavour addresses the issues of increased clinical expenditures associated with discovery, lower Precision in recognition and the manual discovery framework's mobility. System for Detecting Malignant Growths in Melanoma is a deep learning-based predictive model that leverages thermoscope pictures.

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22

Han, Jiwoon, and Daeil Kwon. "Transfer Learning-based Adaptive Diagnosis for Power Plants under Varying Operating Conditions." PHM Society European Conference 8, no. 1 (2024): 6. http://dx.doi.org/10.36001/phme.2024.v8i1.4096.

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Transfer learning is a method that transfers knowledge learned from a source domain to a similar target domain to improve learning. In power plants, obtaining sufficient anomaly data is difficult due to the characteristics of the systems. Transfer learning enables learning with only a small amount of data from the target domain by using a model trained in a similar domain. By applying transfer learning, models developed for one power plant can be expanded and used in other power plants where available data are limited. Using actual data from an operating combined-cycle power plant, an anomaly diagnosis model was developed and tested. Its applicability to different operating conditions and anomaly cases was evaluated through transfer learning. The fine-tuned pre-trained model was effectively adapted with limited target domain data. Transfer learning was applied despite the limitations of data and distribution differences. The expandability of anomaly diagnosis models to different power plant systems was demonstrated by applying transfer learning.

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23

Ruchik, Kashyapkumar Thaker. "Reinforcement Learning in Robotics: Exploring Sim-to-Real Transfer, Imitation Learning, and Transfer Learning Techniques." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 10, no. 5 (2024): 1–7. https://doi.org/10.5281/zenodo.14001716.

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Reinforcement learning (RL) has recently emerged as a transformative approach in robotics, facilitating the development of intelligent systems capable of learning complex tasks through trial and error. This paper presents a comprehensive review of RL applications in robotics, emphasizing the critical challenge of sim-to-real transfer, which arises from the inherent differences between simulated environments and real-world scenarios. Due to the difficulties associated with gathering real-world data, including sample inefficiency and high costs, simulation environments serve as essential training grounds for robotic agents. However, the performance of these agents often degrades when policies are transferred to real robots, necessitating ongoing research to bridge this gap.I explore various methods aimed at improving policy transfer, including domain randomization, domain adaptation, imitation learning, meta-learning, and knowledge distillation. By categorizing recent advancements and highlighting key application areas—such as air-based, underwater, and land-based robotics— I provide a structured overview of the current state of the field. Furthermore, I discuss significant opportunities and challenges associated with these methodologies and propose future research directions. As the robotics landscape evolves, leveraging AI to create fully autonomous systems that mimic human learning patterns remains a priority. This survey serves as a guiding resource for researchers seeking to advance the capabilities of robotic systems through RL, ultimately contributing to the development of more sophisticated, adaptable, and capable autonomous robots.

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24

Rani, S. V. Jansi. "Plant Disease Detection using Transfer Learning in Precision Agriculture." AMBIENT SCIENCE 9, no. 3 (2022): 34–39. http://dx.doi.org/10.21276/ambi.2022.09.3.ta02.

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25

Hurt, Brian, Meagan A. Rubel, Evan M. Masutani, et al. "Radiologist-supervised Transfer Learning." Journal of Thoracic Imaging 37, no. 2 (2021): 90–99. http://dx.doi.org/10.1097/rti.0000000000000618.

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26

Ford, J. Kevin. "Defining Transfer of Learning." Adult Learning 5, no. 4 (1994): 22–30. http://dx.doi.org/10.1177/104515959400500412.

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27

Steenhuis, Harm-Jan, and Erik J. De Bruijn. "Technology Transfer and Learning." Technology Analysis & Strategic Management 14, no. 1 (2002): 57–66. http://dx.doi.org/10.1080/09537320220125883.

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28

Hu, Junlin, Jiwen Lu, Yap-Peng Tan, and Jie Zhou. "Deep Transfer Metric Learning." IEEE Transactions on Image Processing 25, no. 12 (2016): 5576–88. http://dx.doi.org/10.1109/tip.2016.2612827.

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29

Larsen-Freeman, Diane. "Transfer of Learning Transformed." Language Learning 63 (February 13, 2013): 107–29. http://dx.doi.org/10.1111/j.1467-9922.2012.00740.x.

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30

Liu, Xiaobo. "Ensemble Inductive Transfer Learning." Journal of Fiber Bioengineering and Informatics 8, no. 1 (2015): 105–15. http://dx.doi.org/10.3993/jfbi03201510.

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31

Ashburner, Jill, Jenny Ziviani, Sylvia Rodger, et al. "Improving Transfer of Learning." Journal of Continuing Education in the Health Professions 35, no. 4 (2015): 270–77. http://dx.doi.org/10.1097/ceh.0000000000000000.

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32

Мисбахова, А. Г. "Acculturation as learning transfer." International Journal of Medicine and Psychology 7, no. 4 (2024): 120–27. http://dx.doi.org/10.58224/2658-3313-2024-7-4-120-127.

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проблема аккультурации, приобретающая особую актуальность в эпоху все более усложняющихся форм межкультурного взаимодействия, за последние десятилетия претерпела заметное научное развитие: от поисков надежного определения феномена до моделирования его структуры. Практически во всех научных школах и направлениях аккультурация и понимание специфики той реальности, которая описывается данным понятием, непосредственно определяются либо через культуру, либо через ее отдельные черты или виды. В данном контексте возможны различные модификации и подходы, однако в любом случае аккультурация – это взаимодействие двух и более субъектов (групп и личностей) различных культур, которое приводит к культурным, психологическим, социальным, политическим и другим изменениям в одной из взаимодействующих сторон. Автор предлагает осознать логику моделирования аккультурации, основывающейся на понимании аккультурации как деятельности, на принципах теории научения и конктруктивистском толковании приобретения знаний. the problem of acculturation, which is becoming especially relevant in an era of increasingly complex forms of intercultural interaction, has undergone significant scientific development over the past decades: from the search for a reliable definition of the phenomenon to modeling its structure. In almost all scientific schools and directions, acculturation and understanding of the specifics of the reality that is described by this concept are directly determined either through culture or through its individual features or types. In this context, various modifications and approaches are possible, but in any case, acculturation is the interaction of two or more subjects (groups and individuals) of different cultures, which leads to cultural, psychological, social, political and other changes in one of the interacting parties. The author proposes to understand the logic of modeling acculturation, based on the understanding of acculturation as an activity, on the principles of learning theory and the constructivist interpretation of knowledge acquisition.

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33

Kawalilak, Colleen Anne. "Successful Transfer of Learning." Canadian Journal for the Study of Adult Education 25, no. 1 (2012): 75–76. http://dx.doi.org/10.56105/cjsae.v25i1.359.

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34

Hu, Xuegang, Jianhan Pan, Peipei Li, Huizong Li, Wei He, and Yuhong Zhang. "Multi-bridge transfer learning." Knowledge-Based Systems 97 (April 2016): 60–74. http://dx.doi.org/10.1016/j.knosys.2016.01.016.

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35

Karbalayghareh, Alireza, Xiaoning Qian, and Edward R. Dougherty. "Optimal Bayesian Transfer Learning." IEEE Transactions on Signal Processing 66, no. 14 (2018): 3724–39. http://dx.doi.org/10.1109/tsp.2018.2839583.

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Liu, Xiaobo, Zhentao Liu, Guangjun Wang, Zhihua Cai, and Harry Zhang. "Ensemble Transfer Learning Algorithm." IEEE Access 6 (2018): 2389–96. http://dx.doi.org/10.1109/access.2017.2782884.

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37

Hassan Mahmud, M. M. "On universal transfer learning." Theoretical Computer Science 410, no. 19 (2009): 1826–46. http://dx.doi.org/10.1016/j.tcs.2009.01.013.

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Chun-Wei Seah, Ivor W. Tsang, and Yew-Soon Ong. "Transfer Ordinal Label Learning." IEEE Transactions on Neural Networks and Learning Systems 24, no. 11 (2013): 1863–76. http://dx.doi.org/10.1109/tnnls.2013.2268541.

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Ding, Zhengming, Ming Shao, and Yun Fu. "Incomplete Multisource Transfer Learning." IEEE Transactions on Neural Networks and Learning Systems 29, no. 2 (2018): 310–23. http://dx.doi.org/10.1109/tnnls.2016.2618765.

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Wang, Longhan, Yifan Sun, and Xiangdong Zhang. "Quantum Adversarial Transfer Learning." Entropy 25, no. 7 (2023): 1090. http://dx.doi.org/10.3390/e25071090.

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Adversarial transfer learning is a machine learning method that employs an adversarial training process to learn the datasets of different domains. Recently, this method has attracted attention because it can efficiently decouple the requirements of tasks from insufficient target data. In this study, we introduce the notion of quantum adversarial transfer learning, where data are completely encoded by quantum states. A measurement-based judgment of the data label and a quantum subroutine to compute the gradients are discussed in detail. We also prove that our proposal has an exponential advantage over its classical counterparts in terms of computing resources such as the gate number of the circuits and the size of the storage required for the generated data. Finally, numerical experiments demonstrate that our model can be successfully trained, achieving high accuracy on certain datasets.

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Lavine, Barry K., Karl S. Booksh, and Sharon L. Neal. "Transductive and Transfer Learning." Journal of Experimental and Theoretical Analyses 2, no. 2 (2024): 56–57. http://dx.doi.org/10.3390/jeta2020005.

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Imran, Sheik, and Pradeep N. "A Comprehensive Review on Machine Learning and Transfer Learning Approaches in Liver Tumor Classification." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1034–38. http://dx.doi.org/10.21275/sr24723105503.

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Xu, Yao, Min Xia, Kai Hu, Siyi Zhou, and Liguo Weng. "Style Transfer Review: Traditional Machine Learning to Deep Learning." Information 16, no. 2 (2025): 157. https://doi.org/10.3390/info16020157.

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Style transfer is a technique that learns style features from different domains and applies these features to other images. It can not only play a role in the field of artistic creation but also has important significance in image processing, video processing, and other fields. However, at present, style transfer still faces some challenges, such as the balance between style and content, the model generalization ability, and diversity. This article first introduces the origin and development process of style transfer and provides a brief overview of existing methods. Next, this article explores research work related to style transfer, introduces some metrics used to evaluate the effect of style transfer, and summarizes datasets. Subsequently, this article focuses on the application of the currently popular deep learning technology for style transfer and also mentions the application of style transfer in video. Finally, the article discusses possible future directions for this field.

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Silva, Felipe Leno da, and Anna Helena Reali Costa. "Transfer Learning for Multiagent Reinforcement Learning Systems." Synthesis Lectures on Artificial Intelligence and Machine Learning 15, no. 3 (2021): 1–129. http://dx.doi.org/10.2200/s01091ed1v01y202104aim049.

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Rajkumar, R., Arnav Kaushal, and Aishik Saha. "Accelerating Machine Learning Research Using Transfer Learning." Indian Journal of Computer Science 3, no. 2 (2018): 7. http://dx.doi.org/10.17010/ijcs/2018/v3/i2/123212.

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Tamaazousti, Youssef, Herve Le Borgne, Celine Hudelot, Mohamed-El-Amine Seddik, and Mohamed Tamaazousti. "Learning More Universal Representations for Transfer-Learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 9 (2020): 2212–24. http://dx.doi.org/10.1109/tpami.2019.2913857.

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Lewis, Kyle, Donald Lange, and Lynette Gillis. "Transactive Memory Systems, Learning, and Learning Transfer." Organization Science 16, no. 6 (2005): 581–98. http://dx.doi.org/10.1287/orsc.1050.0143.

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Iqbal, Muhammad Shahid, Bin Luo, Tamoor Khan, Rashid Mehmood, and Muhammad Sadiq. "Heterogeneous transfer learning techniques for machine learning." Iran Journal of Computer Science 1, no. 1 (2018): 31–46. http://dx.doi.org/10.1007/s42044-017-0004-z.

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Zhang, Quanqi, Chengwei Wu, Haoyu Tian, Yabin Gao, Weiran Yao, and Ligang Wu. "Safety reinforcement learning control via transfer learning." Automatica 166 (August 2024): 111714. http://dx.doi.org/10.1016/j.automatica.2024.111714.

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Shin, Joonseok, Kyoung-hwan Kim, and Jiyoung Kim. "Structural relationships between learning motivation, transfer design, learning transfer and learning persistence: Focusing on learners of KIRD research ethics education." Korean Association For Learner-Centered Curriculum And Instruction 24, no. 23 (2024): 310–23. https://doi.org/10.22251/jlcci.2024.24.23.310.

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Objectives This study aims to empirically analyze the relationship between learning motivation, transfer design, learning transfer, learning persistence. Methods An empirical analysis was conducted by a survey with 309 graduates of the Cyber Research Ethics Education program, verified the model fit, relationships among latent variables, and mediation effects using SPSS and MPlus software within the framework of structural equation modeling (SEM). Results The following results were derived: First, the study found that learning motivation and transfer design had a positive impact on learning transfer. Second, although learning motivation affected learning persistence, transfer design did not directly affect learning persistence. Third, learning transfer had a statistically significant impact on learning persistence. Fourth, we found that learning transfer mediated the influence of learning motivation and transfer design on learning persistence. Conclusions This study identified the importance of designing educational content that considers learning motivation and transfer design to enhance the transfer of learning in research ethics education. Additionally, learning motivation and transfer design through learning transfer are necessary to increase learners' intention to continue learning.

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