Summary of Source-free Domain Adaptation with Diffusion-guided Source Data Generation, by Shivang Chopra et al.
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
by Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed DMSFDA method leverages pre-trained text-to-image diffusion models for source-free domain adaptation, allowing for the generation of contextually relevant, domain-specific images. By fine-tuning the model to generate source domain images using features from target images, and then applying a diffusion model-based image mixup strategy, significant improvements in SFDA performance are achieved across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to use special models called Diffusion Models for Source-Free Domain Adaptation. It helps computers learn from one type of data and apply it to another without needing more training data. The method uses a pre-trained model that’s fine-tuned to create new images that are similar to the ones in the target domain. This is done by mixing and matching features from both domains. The results show that this approach can greatly improve performance on tasks like image classification. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Domain adaptation * Fine tuning * Image classification
