Transfer Learning in CNNs for Image Recognition in RGB Images

• Author(s): Raj Agarwal ; Kunal Doshi ; Ayushi Upreti ; Aniket Tripathi ; Shruti Sinha
• Paper ID: 1708846
• Page: 550-564
• Published Date: 31-12-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 6 December-2023

Abstract

The advancement in computer vision technologies has made Convolutional Neural Networks (CNNs) a fundamental building block in image recognition applications. Successful yet, training CNNs from scratch is mostly infeasible due to the fact that training CNNs necessitates huge amounts of labeled data as well as enormous computational power. Transfer learning addresses both gaps by allowing the pre-trained architectures to be leveraged to fine-tune them in new image recognition tasks with little data and computational power. This paper introduces transfer learning in CNN-based image recognition with special emphasis on RGB images using well-known architectures VGGNet, ResNet, and Inception. Through them, it is described how transfer learning enables efficient and effective image classification using little data and computational power. Two major methods—feature extraction as well as fine-tuning—and their application and realization as well as usage in different situations are described thoroughly. The paper further identifies key challenges such as domain mismatch, overfitting, and computational limitations as well as suggests potential optimizations to alleviate them. Experiments confirm that transfer learning drastically improves model performance while reducing training time as well as data requirements. Findings attest to the effectiveness as well as flexibility and versatility provided by transfer learning rendering it a useful technique to apply on different occasions and academic and business settings alike. Finally but not least, this work highlights how transfer learning democratizes deep learning by enabling high-performance image recognition even in data-poor situations enabling greater deployment and application of AI-powered solutions in various research and industry environments.

Keywords

Transfer Learning, Convolutional Neural Networks (CNN), Image Classification, Analysis of RGB Images, Feature Extraction Methods, Fine-Tuning Methods, Deep Learning Optimization Methods, Applications in Computer Vision, Pre-trained CNN Models, Reuse and Adjustment of Models, Efficient Learning using Fewer Data, Generalization of Neural Network, Detection of Visual Patterns, Domain Adaptation, Training Efficiency, ResNet, VGGNet, Inception Architectures, Learning using Small Datasets, Accessibility to AI

Citations

IRE Journals:
Raj Agarwal , Kunal Doshi , Ayushi Upreti , Aniket Tripathi , Shruti Sinha "Transfer Learning in CNNs for Image Recognition in RGB Images" Iconic Research And Engineering Journals Volume 7 Issue 6 2023 Page 550-564

IEEE:
Raj Agarwal , Kunal Doshi , Ayushi Upreti , Aniket Tripathi , Shruti Sinha "Transfer Learning in CNNs for Image Recognition in RGB Images" Iconic Research And Engineering Journals, 7(6)