In this study, we address the problem of unsupervised transductive transfer learning for image feature extraction and representation. While transfer learning has shown promising results in various domains, its application to image feature extraction in an unsupervised transductive setting remains relatively unexplored. The research gap lies in the scarcity of methods that can effectively learn meaningful image representations without access to labeled data in the target domain, hindering the broader applicability of transfer learning in computer vision. Our research seeks to bridge this gap by proposing a novel framework that leverages unsupervised feature learning to enhance the adaptability of models across different image domains, thus contributing to the advancement of transfer learning in the field of computer vision. Experimental results demonstrate the effectiveness of our method in addressing this critical research gap and its potential for real-world applications.
Logeshwari Dhavamani1, A. Rajavel2, Subhadra Mishra3, Komal B Umare4
St. Joseph College of Engineering, India1, Kamaraj College of Engineering and Technology, India2, Odisha University of Agriculture and Technology, India3, Shri Ramdeobaba College of Engineering and Management, India4
Unsupervised, Transductive Transfer Learning, Image Feature Extraction, Representation, Deep Neural Networks
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 1 , Pages: 3079 - 3086 )
Date of Publication :
August 2023
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344
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