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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Application of Deep Learning Algorithms to Visual Communication Courses.

Zewen Wang¹, Jiayi Li², Jieting Wu³

¹Pan Tianshou College of Architecture, Art and Design, Ningbo University, Ningbo, China.

Frontiers in Psychology

|October 18, 2021

Summary

This summary is machine-generated.

This study combines visual communication courses with deep learning-based image style transfer. The developed system rapidly applies artistic styles to images, enhancing student learning of visual perception and artistic styles.

Keywords:

TensorFlow deep learning fast style transfer network image style transfer visual communication courses

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Area of Science:

Computer Science
Art Education

Background:

Limited research exists on integrating visual communication courses with image style transfer techniques.
Image style transfer can offer a vivid method for students to grasp perceptual differences across various artistic styles.

Purpose of the Study:

To develop a collaborative application merging visual communication principles with deep learning-based image style transfer.
To enhance student comprehension of artistic styles and improve learning efficiency in visual communication courses.

Main Methods:

A deep learning-based image style transfer method utilizing a fast transfer network was designed.
The system separates training and execution for accelerated image rendering, employing TensorFlow for network construction.
Six diverse image categories (landscape, architecture, character, animal, cartoon, hand-painted) were used for style conversion testing.

Main Results:

The style transfer method demonstrated excellent effects across most image types, with minor rendering challenges on complex details.
Increased network iterations improved image content and style fidelity.
Real-time image style transmission was achieved in under 1 second, enhancing stylization effects and image quality.

Conclusions:

The developed image style transfer system effectively improves students' understanding of diverse artistic styles within visual communication curricula.
This integration enhances learning efficiency by providing a dynamic and engaging method for exploring visual aesthetics.