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Related Experiment Video

Updated: Jun 3, 2025

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Domain-guided conditional diffusion model for unsupervised domain adaptation.

Yulong Zhang¹, Shuhao Chen², Weisen Jiang³

¹State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China.

Neural Networks : the Official Journal of the International Neural Network Society

|January 8, 2025

Summary

This summary is machine-generated.

Domain-guided Conditional Diffusion Models (DCDM) enhance deep learning model performance on new tasks by generating realistic target data. This approach improves Unsupervised Domain Adaptation (UDA) even with significant domain shifts and limited data.

Keywords:

Diffusion models Transfer learning Unsupervised domain adaptation

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning models struggle with limited transferability to new scenarios.
Unsupervised Domain Adaptation (UDA) aims to bridge this gap by learning domain-invariant features.
Existing UDA methods face challenges due to large domain shifts and scarce target data.

Purpose of the Study:

To propose a novel Domain-guided Conditional Diffusion Model (DCDM) for enhanced Unsupervised Domain Adaptation.
To generate high-fidelity target domain samples to facilitate domain transfer.
To address limitations posed by significant domain shifts and limited target domain data in UDA.

Main Methods:

Developed a Domain-guided Conditional Diffusion Model (DCDM).
Incorporated class information to control generated sample labels.
Utilized a domain classifier to steer generated samples towards the target domain.

Main Results:

DCDM successfully generates high-fidelity target domain samples.
The proposed method significantly improves performance in Unsupervised Domain Adaptation.
Extensive experiments on various benchmarks validate the effectiveness of DCDM.

Conclusions:

DCDM offers a powerful approach to overcome domain shift challenges in deep learning.
The generation of target domain samples simplifies the transfer learning process.
DCDM demonstrates substantial performance gains, advancing the field of UDA.