Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023)

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Reform of Talent Cultivation Based on Deep Learning Technology

Authors
Chunwei Tian1, 2, Bo Li3, *, Jiangbin Zheng1, *, Wei Lu1, Peirong Guo1, Bing Li1
1School of Software, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, People’s Republic of China
2Research and Development Institute, Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, People’s Republic of China
3School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, People’s Republic of China
*Corresponding author. Email: libo803@nwpu.edu.cn
*Corresponding author. Email: zhengjiangbin@nwpu.edu.cn
Corresponding Authors
Bo Li, Jiangbin Zheng
Available Online 4 July 2023.
DOI
10.2991/978-94-6463-192-0_103How to use a DOI?
Keywords
Talent Cultivation; Image denoising; Teaching reform; Neural network
Abstract

In modern times, since talents with a background in new engineering in colleges and universities play a vital role in society, it is further reform and practice that universities need to strengthen in engineering education. Taking the software engineering talent training program in universities as an example, this paper analyzes the current situation of software engineering talent cultivation against the background of new engineering. In light of the emerging issues, this paper introduces the student-centered education concept. Then, according to the characteristics of software engineering majors, relevant suggestions in terms of curriculum system, teaching mode, and teaching management are offered, which aim to foster the development of world-class software engineering talents. Besides, this paper designs an image denoising module, as well as a dense residual dynamic region-aware convolutional neural network (DRDRNet), for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. Quantitative and qualitative experiments have demonstrated the denoising performance of the proposed module.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
4 July 2023
ISBN
10.2991/978-94-6463-192-0_103
ISSN
2667-128X
DOI
10.2991/978-94-6463-192-0_103How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Chunwei Tian
AU  - Bo Li
AU  - Jiangbin Zheng
AU  - Wei Lu
AU  - Peirong Guo
AU  - Bing Li
PY  - 2023
DA  - 2023/07/04
TI  - Reform of Talent Cultivation Based on Deep Learning Technology
BT  - Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023)
PB  - Atlantis Press
SP  - 802
EP  - 808
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-192-0_103
DO  - 10.2991/978-94-6463-192-0_103
ID  - Tian2023
ER  -