Authors: Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha

Published on: February 07, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.04929

Summary

• What is new: Introduces a novel approach using Diffusion Models for Source-Free Domain Adaptation to enhance generalizability.
• Why this is important: The challenge of adapting a model trained on a source domain to perform well on a target domain without access to the source data.
• What the research proposes: Fine-tuning a text-to-image diffusion model to generate source domain images guided by features from target images and using unsupervised domain adaptation techniques for alignment.
• Results: Significant improvements in SFDA performance demonstrated across various datasets such as Office-31, Office-Home, and VisDA.

Technical Details

Technological frameworks used: Source-Free Domain Adaptation using Diffusion Models

Models used: Pre-trained text-to-image diffusion models

Data used: Office-31, Office-Home, and VisDA datasets

Potential Impact

This research could impact image-based model development companies, visual content creation tools, and machine learning platforms focusing on domain adaptation services.

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