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Generated Image Editing Method Based on Global-Local Jacobi Disentanglement for Machine Learning.

Jianlong Zhang1, Xincheng Yu1, Bin Wang1

  • 1School of Electronic Engineering, Xidian University, Xi'an 710071, China.

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|February 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel global-local Jacobi disentanglement method for editing StyleGAN2-generated images. It enhances semantic editing accuracy and speed, improving machine learning and big data sample enhancement.

Keywords:
Jacobi orthogonal regularizationStyleGAN2image editingunsupervised methodsweight matrix eigen decomposition

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Accurate semantic editing of generated images is crucial for machine learning and big data enhancement.
  • The StyleGAN2 network faces challenges with semantic entanglement in its latent space.

Purpose of the Study:

  • To address semantic entanglement in StyleGAN2's latent space.
  • To propose an improved generated image editing method based on global-local Jacobi disentanglement.

Main Methods:

  • Global disentanglement: Extracting StyleGAN2 weight matrices, using eigen decomposition for semantic attribute direction vectors, and employing Jacobi orthogonal regularization search.
  • Local disentanglement: Designing a local contrast regularized loss function and using Jacobi orthogonal regularization search with local area prior masks.

Main Results:

  • The proposed method significantly improves the speed of the Jacobi orthogonal regularization search algorithm.
  • It enhances the proportion of effective semantic attribute editing directions.
  • Achieves state-of-the-art (SOTA) performance in semantic attribute disentangled metrics.
  • Discovers more accurate editing directions compared to mainstream unsupervised methods.

Conclusions:

  • The global-local Jacobi disentanglement method effectively addresses semantic entanglement in StyleGAN2.
  • This approach offers superior accuracy and efficiency for generated image editing.
  • It holds significant potential for advancing machine learning and big data applications.