Applications and Challenges of Deep Learning in Image Recognition

Tianran Li

2024

Abstract

In image recognition, deep learning has offered great progression throughout the last several years through allowing machines to learn intricate aspects of an image or visual data advancing various sectors like; healthcare, autonomous systems, and security to mention a few. Convolutional neural networks (CNNs) have been spearheading these innovations but challenges including restricted data accessibility, numerical complexity and model explainability hinder. That comes with obstacles including data limitations and data quality issues, however many of these have been solved using methods like synthetic data creation, transfer learning alongside general model refinement. Therefore, there is a need to unlock the blackbox and offer methods through which trust in deep learning models can be availed particularly in areas that are very sensitive. Furthermore, it is also identified that model compression as well as adversarial training provide the solutions for increasing efficiency and robustness. The paper focuses on discussing the principal fields that attract Deep learning (DL) to image recognition, the main difficulties it encounters, and new breakthroughs designed to improve model performance and adaptability. Consequently, the further development of deep learning algorithms in the field of image recognition will be defined by increasing their data efficiency, the optimization of model interpretability, and increasing the computational efficiency of the techniques used.

Paper Citation

in Harvard Style

Li T. (2024). Applications and Challenges of Deep Learning in Image Recognition. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 252-256. DOI: 10.5220/0013515300004619

in Bibtex Style

@conference{daml24,
author={Tianran Li},
title={Applications and Challenges of Deep Learning in Image Recognition},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={252-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013515300004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Applications and Challenges of Deep Learning in Image Recognition
SN - 978-989-758-754-2
AU - Li T.
PY - 2024
SP - 252
EP - 256
DO - 10.5220/0013515300004619
PB - SciTePress