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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Image-to-Image Translation With Disentangled Latent Vectors for Face Editing.

Yusuf Dalva, Hamza Pehlivan, Oyku Irmak Hatipoglu

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|August 24, 2023

Summary

This study introduces a new framework for facial attribute editing using disentangled latent directions. The method enables controllable and disentangled attribute manipulation in images, significantly improving over existing techniques.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Facial attribute editing requires targeted control over specific attributes while preserving others.
Existing image-to-image translation methods struggle with disentanglement and controllable editing strength.

Purpose of the Study:

To develop an image-to-image translation framework for facial attribute editing.
To achieve disentangled and interpretable latent directions for controllable attribute manipulation.
To preserve facial attributes not targeted for editing.

Main Methods:

Latent space factorization inspired by pre-trained Generative Adversarial Networks (GANs).
Learning orthogonal linear directions for each attribute with orthogonality constraints and disentanglement losses.
Utilizing an encoder-decoder architecture with attention-based skip connections for image projection.

Main Results:

The proposed method demonstrates significant improvements over state-of-the-art image translation and GAN editing techniques.
Achieved controllable strength and disentanglement in facial attribute edits.
Preserved non-edited attributes effectively during manipulation.

Conclusions:

The framework offers a robust solution for disentangled and controllable facial attribute editing.
Latent space factorization provides an effective approach for interpretable image manipulation.
The method advances the field of image-to-image translation for semantic editing tasks.