Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cycloalkanes02:28

Cycloalkanes

14.2K
Cycloalkanes are saturated cyclic hydrocarbons with carbon atoms arranged in the form of rings. They have two fewer hydrogen atoms than the corresponding acyclic alkane; therefore, their general formula is CnH2n. The structural formulas of cycloalkanes are simplified using the line-angle representation. The regular polygons are used to represent the cycloalkane rings, with each side representing a carbon-carbon bond.
The IUPAC nomenclature of cycloalkanes follows similar rules that apply to...
14.2K
Cycloaddition Reactions: Overview01:16

Cycloaddition Reactions: Overview

2.4K
Cycloadditions are one of the most valuable and effective synthesis routes to form cyclic compounds. These are concerted pericyclic reactions between two unsaturated compounds resulting in a cyclic product with two new σ bonds formed at the expense of π bonds. The [4 + 2] cycloaddition, known as the Diels–Alder reaction, is the most common. The other example is a [2 + 2] cycloaddition.
2.4K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

13.7K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
13.7K
Cycloaddition Reactions: MO Requirements for Photochemical Activation01:12

Cycloaddition Reactions: MO Requirements for Photochemical Activation

1.7K
Some cycloaddition reactions are activated by heat, while others are initiated by light. For example, a [2 + 2] cycloaddition between two ethylene molecules occurs only in the presence of light. It is photochemically allowed but thermally forbidden.
1.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Enhancing crop yield prediction accuracy with a novel interpretable deep learning model: MHCNN-LSTM-MHA.

Scientific reports·2026
Same author

Nanomedicine for venous thrombosis: Insights from an inflammatory perspective.

Biomaterials·2025
Same author

An empirical evaluation of dimensionality reduction and class balancing for medical text classification.

Scientific reports·2025
Same author

Dual-Wavelength Responsive Hydrogel Glue with Visible-Light Bonding and UV-Triggered Debonding via Ortho-Nitrobenzyl Cleavage.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Feasibility and case studies on converting small hydropower stations to pumped storage.

Scientific reports·2025
Same author

An intelligent spam detection framework using fusion of spammer behavior and linguistic.

PloS one·2025

Related Experiment Video

Updated: May 5, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K

Visual resource extraction and artistic communication model design based on improved CycleGAN algorithm.

Anyu Yang1, Muhammad Kashif Hanif2

  • 1International School of Arts, Dalian University of Foreign Languages, Dalian, Liaoning, China.

Peerj. Computer Science
|April 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced CycleGAN model with an attention mechanism for improved image style transfer in art education. The ATT-CycleGAN model achieves superior results, offering valuable insights for future research in style transfer and image segmentation.

Keywords:
Attention mechanismCycleGANGANImage style migrationVisual resource and artistic communication

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

Related Experiment Videos

Last Updated: May 5, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.0K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
07:11

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

1.5K

Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence in Art Education

Background:

  • Image style transfer enables the fusion of artistic elements for novel creations.
  • Existing methods require enhancement for improved quality and precision in style conversion.

Purpose of the Study:

  • To introduce an attention-enhanced CycleGAN (ATT-CycleGAN) model for superior image style transfer.
  • To improve the quality and precision of style conversion in art education applications.

Main Methods:

  • The ATT-CycleGAN model incorporates an attention mechanism within the CycleGAN framework.
  • Feature maps undergo encoding residual blocks and an attention module with channel attention via multi-weight optimization.
  • Transfer learning techniques are utilized for efficient model parameter initialization during training.

Main Results:

  • The proposed ATT-CycleGAN model demonstrates superior performance in image style transfer.
  • Significant improvements in Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were observed compared to traditional CycleGAN.
  • On Places365 and selfi2anime datasets, SSIM increased by 3.19% and 1.31%, and PSNR by 10.16% and 5.02% respectively.

Conclusions:

  • The ATT-CycleGAN model offers enhanced algorithmic support for art education and style transfer research.
  • The findings provide crucial references for future advancements in image segmentation and artistic style conversion.
  • The study highlights the effectiveness of attention mechanisms in deep learning for image processing tasks.