Summary of Top-k Pairwise Ranking: Bridging the Gap Among Ranking-based Measures For Multi-label Classification, by Zitai Wang et al.
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
by Zitai Wang, Qianqian Xu, Zhiyong Yang, Peisong Wen, Yuan He, Xiaochun Cao, Qingming Huang
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper introduces Top-K Pairwise Ranking (TKPR), a novel measure for multi-label ranking in visual tasks. This measure aims to address the inconsistency issue in evaluating model performances using existing measures. The authors demonstrate that TKPR is compatible with existing ranking-based measures and propose an empirical surrogate risk minimization framework for TKPR, which enjoys convex surrogate losses and has theoretical support from Fisher consistency. Furthermore, the paper establishes a sharp generalization bound for the proposed framework using data-dependent contraction. Empirical results on benchmark datasets validate the effectiveness of the proposed framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to measure how well models do in ranking visual tasks. It’s like trying to decide which pictures are the best, and it’s important because this technique has many uses. The problem is that different measures might give different answers, so the authors came up with a new one called TKPR. They show that TKPR works well with existing measures and developed a way to use it to make models better. They also tested their idea on real datasets and found that it works. |
Keywords
* Artificial intelligence * Generalization
