Summary of Task Groupings Regularization: Data-free Meta-learning with Heterogeneous Pre-trained Models, by Yongxian Wei et al.
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
by Yongxian Wei, Zixuan Hu, Li Shen, Zhenyi Wang, Yu Li, Chun Yuan, Dacheng Tao
First submitted to arxiv on: 26 May 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 introduces Data-Free Meta-Learning (DFML), a technique to rapidly adapt pre-trained models to new unseen tasks without accessing their original data. Current methods overlook the heterogeneity among pre-trained models, leading to performance degradation due to task conflicts. The authors empirically and theoretically identify and analyze model heterogeneity in DFML, finding a trade-off between homogeneity and overfitting risk. They propose Task Groupings Regularization (TGR) that benefits from model heterogeneity by grouping and aligning conflicting tasks. TGR embeds pre-trained models into a task space to compute dissimilarity, groups heterogeneous models together based on this measure, and introduces implicit gradient regularization within each group to mitigate potential conflicts. The authors demonstrate the superiority of their approach in multiple benchmarks, tackling challenging multi-domain and multi-architecture scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using pre-trained models without needing the original data. They found that these models are not all alike, which can make it harder to adapt them to new tasks. The authors came up with a way to group similar models together and help them learn from each other. This helps them find common patterns across different tasks. They tested their approach on various datasets and showed it performs better than current methods. |
Keywords
» Artificial intelligence » Meta learning » Overfitting » Regularization
