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Impact of Deep Learning on Transfer Learning: A Review

Mohammed Jameel Barwary and Adnan Abdulazeez
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Mohammed Jameel Barwary: Duhok Polytechnic University, Duhok, Kurdistan Region, Iraq

International Journal of Science and Business, 2021, vol. 5, issue 3, 204-216

Abstract: Transfer learning and deep learning approaches have been utilised in several real-world applications and hierarchical systems for pattern recognition and classification tasks. However, in few of the real-world machine learning situations, this presumption does not sustain since there are instances where training data is costly or tough to gather and there is continually a necessity to produce high-performance learners competent with more easily attained data from diverse fields. The objective of this review is to determine more abstract qualities at the greater levels of the representation, by utilising deep learning to detach the variables in the outcomes, formally outline transfer learning, provide information on present solutions, and appraise applications employed in diverse facets of transfer learning and deep learning. This can be attained by rigorous literature exploration and discussion on all presently accessible techniques and prospective research studies on transfer learning solutions of independent as well as big data scale. The conclusions of this study could be an effectual platform directed at prospective directions for devising new deep learning patterns for different applications and dealing with the challenges concerned.

Keywords: Machine Learning; Transfer Learning; Deep Learning; classifications; Supervised Learning techniques (search for similar items in EconPapers)
Date: 2021
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