Summary of Generalized Coverage For More Robust Low-budget Active Learning, by Wonho Bae et al.
Generalized Coverage for More Robust Low-Budget Active Learning
by Wonho Bae, Junhyug Noh, Danica J. Sutherland
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to active learning in low-budget regimes, building upon the ProbCover method introduced by Yehuda et al. The authors demonstrate that the performance of ProbCover is highly dependent on the choice of a hyperparameter, which can be challenging to tune. To address this issue, they propose a generalized notion of “coverage” that encompasses ProbCover’s objective while allowing for smoother and more robust optimization. The authors also introduce an efficient greedy algorithm, called MaxHerding, to optimize this coverage. In comprehensive experiments, MaxHerding outperforms existing active learning methods on multiple low-budget image classification benchmarks with reduced computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Active learning is a way to improve machine learning models by selecting the most informative data points for training. Researchers have been working on developing algorithms that can do this efficiently and effectively. One algorithm called ProbCover has shown promise, but it’s not perfect. The authors of this paper found that ProbCover relies heavily on one important setting, which can be tricky to get right. To solve this problem, they came up with a new way to measure how well an algorithm is doing. This new approach allows for smoother and more robust optimization, making it better than ProbCover in many cases. |
Keywords
» Artificial intelligence » Active learning » Hyperparameter » Image classification » Machine learning » Optimization
