Summary of Transcending Domains Through Text-to-image Diffusion: a Source-free Approach to Domain Adaptation, by Shivang Chopra et al.
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation
by Shivang Chopra, Suraj Kothawade, Houda Aynaou, Aman Chadha
First submitted to arxiv on: 2 Oct 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 framework for Source-Free Domain Adaptation (SFDA) generates source data using a text-to-image diffusion model trained on target domain samples. This novel approach starts by training a text-to-image diffusion model on labeled target domain samples, which is then fine-tuned using a pre-trained source model to generate samples close to the source data. The artificially generated source data is then aligned with the target domain data using Domain Adaptation techniques, resulting in significant performance improvements of the model on the target domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists developed a new way to adapt models to work better in unfamiliar areas without needing direct access to the original information. They trained a special kind of computer program that generates images based on words and then fine-tuned it using another pre-trained program to create fake data similar to what was originally available. By combining these two approaches with standard techniques, they made the model much more accurate in its new environment. |
Keywords
* Artificial intelligence * Diffusion model * Domain adaptation
