Optimizing Image Processing with CNNs through Transfer Learning: Survey
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Abstract
The field of image processing has been revolutionized by Convolutional Neural Networks (CNNs), which exhibit exceptional capability in feature extraction and accurate image classification. However, training CNNs requires large volumes of annotated data and significant computational resources. Considering these challenges, transfer learning has emerged as a promising approach to reducing the dependence on labeled data and computational resources. Transfer learning involves utilizing knowledge gained from a source task to improve the training process for a target task. This technique has demonstrated considerable benefits; however, it also possesses certain limitations. Consequently, this survey explores the advantages and constraints of transfer learning and the various factors that influence its effectiveness in optimizing image processing using CNNs. Additionally, the survey investigates the most recent advancements and research in the field of transfer learning specifically for image processing with CNNs. In summary, this comprehensive analysis highlights the significance of transfer learning in the context of optimizing image processing with CNNs, providing unique insights into this rapidly evolving domain.
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