Summary of Euda: An Efficient Unsupervised Domain Adaptation Via Self-supervised Vision Transformer, by Ali Abedi et al.
EUDA: An Efficient Unsupervised Domain Adaptation via Self-Supervised Vision Transformer
by Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah
First submitted to arxiv on: 31 Jul 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 Efficient Unsupervised Domain Adaptation (EUDA) framework aims to address the domain shift issue by developing an efficient model that reduces trainable parameters while delivering comparable performance. The EUDA framework employs a self-supervised vision transformer (DINOv2) as a feature extractor, followed by a simplified bottleneck of fully connected layers to refine features for enhanced domain adaptation. Additionally, it utilizes synergistic domain alignment loss (SDAL), which integrates cross-entropy and maximum mean discrepancy losses to balance adaptation. The results demonstrate the effectiveness of EUDA in producing comparable results to state-of-the-art methods while reducing trainable parameters by 42% to 99.7%. This showcases the ability to train the model in a resource-limited environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EUDA is an efficient way to help machines learn from different types of data. Right now, there are many models that try to solve this problem, but they’re often too complex or require too much computer power. The new framework, EUDA, tries to fix this by creating a simpler model that can still do well. It uses a special kind of artificial intelligence called vision transformers and adds some extra steps to make the data more similar between training and testing. This helps the machine learn better from different types of data. The results show that EUDA works as well as other top methods, but with fewer calculations needed. |
Keywords
» Artificial intelligence » Alignment » Cross entropy » Domain adaptation » Self supervised » Unsupervised » Vision transformer
