Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation

• URL: http://arxiv.org/abs/2402.04929v3
• Date: Wed, 26 Jun 2024 20:57:15 GMT
• Title: Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
• Authors: Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha,
• Abstract summary: This paper introduces a novel approach to leverage the generalizability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA) Our proposed DMSFDA method involves fine-tuning a pre-trained text-to-image diffusion model to generate source domain images. We validate our approach through comprehensive experiments across a range of datasets, including Office-31, Office-Home, and VisDA.
• Score: 6.087274577167399
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel approach to leverage the generalizability of Diffusion Models for Source-Free Domain Adaptation (DM-SFDA). Our proposed DMSFDA method involves fine-tuning a pre-trained text-to-image diffusion model to generate source domain images using features from the target images to guide the diffusion process. Specifically, the pre-trained diffusion model is fine-tuned to generate source samples that minimize entropy and maximize confidence for the pre-trained source model. We then use a diffusion model-based image mixup strategy to bridge the domain gap between the source and target domains. We validate our approach through comprehensive experiments across a range of datasets, including Office-31, Office-Home, and VisDA. The results demonstrate significant improvements in SFDA performance, highlighting the potential of diffusion models in generating contextually relevant, domain-specific images.

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