Summary of Task-specific Preconditioner For Cross-domain Few-shot Learning, by Suhyun Kang et al.
Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
by Suhyun Kang, Jungwon Park, Wonseok Lee, Wonjong Rhee
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Task-Specific Preconditioned gradient descent (TSP) method addresses the limitations of current Cross-Domain Few-Shot Learning methods by adapting task-specific parameters using a novel adaptation mechanism. The approach first meta-learns Domain-Specific Preconditioners (DSPs) that capture the characteristics of each meta-training domain, which are then combined with task-coefficients to form the Task-Specific Preconditioner. This preconditioner is applied to gradient descent, making the optimization adaptive to the target task. Empirical evaluations on the Meta-Dataset show that TSP achieves state-of-the-art performance across diverse experimental scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TSP helps machines learn new tasks quickly by adapting how they optimize the learning process. It does this by first learning special tools for each type of data, and then combining those tools with information about the specific task it’s trying to learn. This makes the optimization process more effective and efficient. In tests on a wide range of tasks, TSP performed better than other methods. |
Keywords
» Artificial intelligence » Few shot » Gradient descent » Optimization
