Summary of Domain Generalization Guided by Large-scale Pre-trained Priors, By Zongbin Wang et al.
Domain Generalization Guided by Large-Scale Pre-Trained Priors
by Zongbin Wang, Bin Pan, Shiyu Shen, Tianyang Shi, Zhenwei Shi
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 approach to domain generalization (DG) called Fine-Tune with Large-scale pre-trained Priors (FT-LP). The method incorporates pre-trained models as priors during the fine-tuning process, allowing DG models to maintain their ability to resist domain shift. This is achieved by continuously referencing the pre-trained model at each optimization step. The proposed approach is theoretically verified through a generalization error bound and implemented using an encoder to simulate model distribution. Experimental results on various datasets and DG models demonstrate significant improvements, indicating the effectiveness of FT-LP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to help artificial intelligence (AI) learn from some data it already knows well, but then apply what it learned to new situations where it hasn’t seen those specific details before. To do this, the researchers came up with a new way to train AI models called Fine-Tune with Large-scale pre-trained Priors (FT-LP). This approach helps AI models keep the knowledge they gained from their initial training and use that to make good decisions in new situations. |
Keywords
» Artificial intelligence » Domain generalization » Encoder » Fine tuning » Generalization » Optimization
