Summary of Drive: Dual-robustness Via Information Variability and Entropic Consistency in Source-free Unsupervised Domain Adaptation, by Ruiqiang Xiao et al.
DRIVE: Dual-Robustness via Information Variability and Entropic Consistency in Source-Free Unsupervised Domain Adaptation
by Ruiqiang Xiao, Songning Lai, Yijun Yang, Jiemin Wu, Yutao Yue, Lei Zhu
First submitted to arxiv on: 24 Nov 2024
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 paper proposes a novel framework for Source-Free Unsupervised Domain Adaptation (SFUDA), dubbed DRIVE, which leverages a dual-model architecture to capture diverse target domain characteristics. The approach uses projection gradient descent (PGD) guided by mutual information to focus on high-uncertainty regions and introduces an entropy-aware pseudo-labeling strategy that adjusts label weights based on prediction uncertainty. The adaptation process consists of two stages: aligning the models on stable features using a mutual information consistency loss, followed by dynamically adjusting perturbation levels based on the first stage’s loss. This enhances generalization capabilities and robustness against interference. DRIVE is evaluated on standard SFUDA benchmarks, demonstrating improved adaptation accuracy and stability across complex target domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to help machines learn new things without labeled data, which is important for applications like medical imaging, self-driving cars, and remote sensing. Right now, adapting models to new areas can be tricky because the source data might not be accessible or reliable. The authors propose a new way to do this called DRIVE, which uses two models working together in parallel. One model helps the other by focusing on areas where it’s most uncertain. The approach also adjusts how much “noise” is allowed based on how well the models are doing. This makes the adapted model more robust and able to generalize better. The results show that DRIVE performs better than previous methods on typical benchmarks. |
Keywords
» Artificial intelligence » Domain adaptation » Generalization » Gradient descent » Unsupervised
