Summary of Task Attribute Distance For Few-shot Learning: Theoretical Analysis and Applications, by Minyang Hu et al.
Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
by Minyang Hu, Hong Chang, Zong Guo, Bingpeng Ma, Shiguan Shan, Xilin Chen
First submitted to arxiv on: 6 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 delves into the mysteries of few-shot learning (FSL) by addressing two key questions: how to quantify the relationship between training and novel tasks, and how does this relationship impact adaptation difficulty on novel tasks for different models. The authors propose Task Attribute Distance (TAD), a model-agnostic metric that measures task relatedness and demonstrates its effectiveness in quantifying adaptation difficulty on novel tasks. Experimental results on three benchmarks validate TAD’s ability to capture task relatedness and reflect adaptation difficulty for various FSL methods. Moreover, the proposed TAD metric is applied to data augmentation and test-time intervention, further verifying its utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSL is a way for machines to learn new things with just a few examples by using what they already know from similar tasks. This paper tries to understand how this works by looking at two big questions: how do we measure the connection between old and new tasks, and how does this connection affect how well models adapt to new tasks? The authors introduce a new way to measure task similarity called Task Attribute Distance (TAD) that can be used with different machine learning models. They show that TAD is good at measuring task relatedness and reflecting adaptation difficulty on new tasks for various FSL methods. |
Keywords
* Artificial intelligence * Data augmentation * Few shot * Machine learning
