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Pdf Emerging Trends Of Research On Transfer Of Learning Ed

Leo Migdal
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pdf emerging trends of research on transfer of learning ed

While machine learning has achieved remarkable success across various applications, this success often relies on the assumption that training and testing data originate from the same domain with a shared distribution and feature space. This assumption is frequently violated in real-world scenarios where training data may derive from different distributions, often due to limited access to labeled samples or data collection challenges. This situation necessitates the development of robust methodologies encapsulated in the framework of transfer learning, which aims to enhance learner performance in different domains by leveraging knowledge from a source domain to improve outcomes... This review provides a comprehensive assessment of the evolving landscape of transfer learning, highlighting the need for a detailed literature survey on current approaches and their implications. By categorizing theoretical transfer learning algorithms based on the number of source domains and the handling of incomplete multi-source data, we offer a distinctive perspective that elucidates the practical utility of these methodologies. Our exploration spans various pivotal categories, including single-source/single-target and multi-source/single-target frameworks, while systematically addressing the complexities of incomplete transfer learning and the integration of fuzzy systems to manage uncertainty.

Additionally, we introduce commonly used public datasets in transfer learning and present the outcomes of several key algorithms discussed in this review on these datasets. This empirical perspective illustrates the practical effectiveness of the methodologies and provides insights into their performance under diverse conditions. This extensive analysis summarizes key findings from diverse studies, delineates their interconnections, and highlights their applicability to real-world problems, thereby serving as a valuable resource for researchers navigating the intricate dynamics of transfer learning. This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout. Afridi MJ, Ross A, Shapiro EM (2018) On automated source selection for transfer learning in convolutional neural networks.

Pattern Recognit 73:65–75. https://doi.org/10.1016/j.patcog.2017.07.019 Ahmed SM, Raychaudhuri DS, Paul S, Oymak S, Roy-Chowdhury AK (2021) Unsupervised multi-source domain adaptation without access to source data. In : Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10098–10107. https://doi.org/10.1109/CVPR46437.2021.00997

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While Machine Learning Has Achieved Remarkable Success Across Various Applications,

While machine learning has achieved remarkable success across various applications, this success often relies on the assumption that training and testing data originate from the same domain with a shared distribution and feature space. This assumption is frequently violated in real-world scenarios where training data may derive from different distributions, often due to limited access to labeled s...

Additionally, We Introduce Commonly Used Public Datasets In Transfer Learning

Additionally, we introduce commonly used public datasets in transfer learning and present the outcomes of several key algorithms discussed in this review on these datasets. This empirical perspective illustrates the practical effectiveness of the methodologies and provides insights into their performance under diverse conditions. This extensive analysis summarizes key findings from diverse studies...

Pattern Recognit 73:65–75. Https://doi.org/10.1016/j.patcog.2017.07.019 Ahmed SM, Raychaudhuri DS, Paul S,

Pattern Recognit 73:65–75. https://doi.org/10.1016/j.patcog.2017.07.019 Ahmed SM, Raychaudhuri DS, Paul S, Oymak S, Roy-Chowdhury AK (2021) Unsupervised multi-source domain adaptation without access to source data. In : Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10098–10107. https://doi.org/10.1109/CVPR46437.2021.00997