Summary of Model Selection with Model Zoo Via Graph Learning, by Ziyu Li et al.
Model Selection with Model Zoo via Graph Learning
by Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 TransferGraph framework addresses the challenge of selecting the best pre-trained deep learning (DL) models for fine-tuning on new prediction tasks. By formulating model selection as a graph learning problem, TransferGraph captures essential relationships between models and datasets, leading to improved model performance predictions. This is achieved through the construction of a graph using extensive metadata extracted from models and datasets. Experimental results across 16 real-world datasets demonstrate the framework’s effectiveness in capturing model-dataset relationships, resulting in up to a 32% improvement over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The TransferGraph framework helps find the best pre-trained deep learning (DL) models for new tasks by understanding how they relate to different datasets. This is important because using the right model can make a big difference in how well it works. The current method of choosing models based on simple information doesn’t work well, so researchers developed a new way to look at this problem. They treat it like a puzzle and try to figure out which models are most likely to work well with certain datasets. This helps them choose the best model for each task. |
Keywords
* Artificial intelligence * Deep learning * Fine tuning
