Noise Optimized Conditional Diffusion for Domain Adaptation

• URL: http://arxiv.org/abs/2505.07548v1
• Date: Mon, 12 May 2025 13:28:31 GMT
• Title: Noise Optimized Conditional Diffusion for Domain Adaptation
• Authors: Lingkun Luo, Shiqiang Hu, Liming Chen,
• Abstract summary: Pseudo-labeling is a cornerstone of Unsupervised Domain Adaptation (UDA)<n>We propose textbfNoise textbfOptimized textbfConditional textbfDiffusion for textbfDomain textbfAdaptation (textbfNOCDDA)
• Score: 7.414646586981638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pseudo-labeling is a cornerstone of Unsupervised Domain Adaptation (UDA), yet the scarcity of High-Confidence Pseudo-Labeled Target Domain Samples (\textbf{hcpl-tds}) often leads to inaccurate cross-domain statistical alignment, causing DA failures. To address this challenge, we propose \textbf{N}oise \textbf{O}ptimized \textbf{C}onditional \textbf{D}iffusion for \textbf{D}omain \textbf{A}daptation (\textbf{NOCDDA}), which seamlessly integrates the generative capabilities of conditional diffusion models with the decision-making requirements of DA to achieve task-coupled optimization for efficient adaptation. For robust cross-domain consistency, we modify the DA classifier to align with the conditional diffusion classifier within a unified optimization framework, enabling forward training on noise-varying cross-domain samples. Furthermore, we argue that the conventional \( \mathcal{N}(\mathbf{0}, \mathbf{I}) \) initialization in diffusion models often generates class-confused hcpl-tds, compromising discriminative DA. To resolve this, we introduce a class-aware noise optimization strategy that refines sampling regions for reverse class-specific hcpl-tds generation, effectively enhancing cross-domain alignment. Extensive experiments across 5 benchmark datasets and 29 DA tasks demonstrate significant performance gains of \textbf{NOCDDA} over 31 state-of-the-art methods, validating its robustness and effectiveness.

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