Applications of Transfer Learning techniques in Computer Vision

Main Article Content

B. V. Ramana
B. R. Sarath Kumar

Abstract

Computer vision has experienced a remarkable metamorphosis in recent years, transforming our capacity to extract meaningful information from pictures and movies. This transformation may be credited in large part to the rise of deep learning, specifically deep convolutional neural networks (CNNs), which have exhibited extraordinary skill in tasks such as picture classification, object recognition, and semantic segmentation. However, the effectiveness of deep learning models often depends on having access to large volumes of labeled data, which is not always accessible in realworld applications This study sheds light on the advantages, limitations, and prospects of transfer learning in computer vision through a comprehensive review of state-of-the-art techniques and case studies, emphasizing its vital role in stretching the boundaries of visual recognition and comprehension.

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How to Cite
Ramana, B. V. ., & Kumar, B. R. S. (2020). Applications of Transfer Learning techniques in Computer Vision. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(1), 985–990. https://doi.org/10.17762/turcomat.v11i1.14268
Section
Research Articles

References

P. H. Leung, improving automated construction site monitoring using computer vision and

transfer learning techniques.

H. Yin, improving deep reinforcement learning with advanced exploration and transfer learning

techniques.

Y. Wu and Q. Ji, “Constrained deep transfer feature learning and its applications,” 2016 IEEE

Conference on Computer Vision and Pattern Recognition (CVPR), 2016. doi:10.1109/cvpr.2016.551

M. Brown and M. McNitt-Gray, “Medical image interpretation,” Handbook of Medical Imaging,

Volume 2. Medical Image Processing and Analysis, pp. 399–445.