Deep Learning (CNN) and Transfer Learning: A Review

Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learn...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 2273; no. 1; pp. 12029 - 12038
Main Authors: Gupta, Jaya, Pathak, Sunil, Kumar, Gireesh
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.05.2022
Subjects:
Algorithms
Computer vision
Deep learning
Image processing
Machine learning
Object recognition
Pattern recognition
Physics
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2273/1/012029