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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Unsupervised Exemplar-Domain Aware Image-to-Image Translation.

Yuanbin Fu1, Jiayi Ma2, Xiaojie Guo1

  • 1College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Entropy (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

We introduce the exemplar-domain aware image-to-image translator (EDIT), a novel deep learning model for style transfer. EDIT enables controllable, diverse image-to-image translation across multiple domains and exemplars.

Keywords:
generative adversarial networkimage-to-image translationneural style transferunsupervised learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Image-to-image translation aims to preserve content while altering style.
  • Existing methods often struggle with controllable, many-to-many style transfer across diverse domains.

Purpose of the Study:

  • To develop a novel deep translator for flexible and effective image-to-image translation.
  • To enable controllable, diverse, and many-to-many style transfer in a unified model.

Main Methods:

  • Designed the exemplar-domain aware image-to-image translator (EDIT) with a generator and discriminator.
  • The generator features shared and varied parameters for feature extraction and style transfer.
  • An exemplar-domain aware parameter network guides the re-stylization process.

Main Results:

  • EDIT achieves effective image-to-image translation across multiple domains and exemplars.
  • The model demonstrates flexibility and control in generating diverse results.
  • Experimental results show quantitative and qualitative improvements over state-of-the-art methods.

Conclusions:

  • The proposed EDIT model offers a unified approach to image-to-image translation.
  • EDIT successfully addresses the challenge of controllable many-to-many style transfer.
  • The design provides a robust and advanced solution for diverse image translation tasks.