Unsupervised Domain Adaptation via Domain-Adaptive Diffusion.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Unsupervised Domain Adaptation (UDA) faces significant challenges due to large distribution discrepancies between source and target domains.
• Existing diffusion models are typically designed for Gaussian-to-data distribution conversion, not domain-specific distribution transfer.
• Preserving source domain data semantics during distribution conversion is crucial for accurate target domain classification.

Purpose of the Study:

• To explore diffusion techniques for addressing challenging UDA tasks.
• To develop a method capable of gradually converting data distributions across domains while preserving semantics.
• To enhance classification model capacity during the domain transition process.

Main Methods:

• Propose a novel Domain-Adaptive Diffusion (DAD) module.
• Incorporate a Mutual Learning Strategy (MLS) to facilitate learning during domain transition.
• Decompose large domain gaps into smaller, manageable steps.

Main Results:

• The proposed DAD module and MLS effectively convert data distributions from source to target domains.
• The method enables classification models to learn progressively along the domain transition.
• Demonstrated superior performance over state-of-the-art methods on three standard UDA datasets.

Conclusions:

• The DAD module and MLS successfully mitigate UDA challenges by bridging domain gaps.
• The approach enhances classification model adaptability to target domains.
• This diffusion-based strategy offers a promising direction for future UDA research.