Summary of Unleashing the Power Of Meta-tuning For Few-shot Generalization Through Sparse Interpolated Experts, by Shengzhuang Chen et al.
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
by Shengzhuang Chen, Jihoon Tack, Yunqiao Yang, Yee Whye Teh, Jonathan Richard Schwarz, Ying Wei
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 This paper proposes Sparse MetA-Tuning (SMAT), a novel method that builds upon the success of foundation models for transfer learning in vision. By introducing a subsequent optimization stage, SMAT aims to harness the strengths of both parameter-efficient fine-tuning and meta-learning. The approach involves training sparse mixture-of-experts to automatically isolate subsets of pre-trained parameters for each task, leading to improved out-of-distribution (OOD) performance. Experimental results on Meta-Dataset show that SMAT achieves state-of-the-art results in both zero-shot and gradient-based adaptation settings, outperforming previous methods like meta-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a breakthrough in how we can use pre-trained models for new tasks. Usually, these models are fine-tuned to work well on specific problems, but they often struggle when faced with unknown situations. The authors introduce a new method called SMAT that helps the model adapt better to out-of-the-distribution tasks. They tested it and found it works really well, beating previous methods in many cases. |
Keywords
* Artificial intelligence * Fine tuning * Meta learning * Mixture of experts * Optimization * Parameter efficient * Transfer learning * Zero shot
