Summary of A Comprehensive Review Of Machine Learning Advances on Data Change: a Cross-field Perspective, by Jeng-lin Li et al.
A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective
by Jeng-Lin Li, Chih-Fan Hsu, Ming-Ching Chang, Wei-Chao Chen
First submitted to arxiv on: 20 Feb 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 This paper reviews recent advancements in artificial intelligence (AI) technologies that address dynamic data changes, which severely impact AI model performance. The authors identify two related research fields: domain shift and concept drift, both aiming to solve distribution shift and non-stationary data stream problems. By regrouping these fields into a single problem, the “data change problem,” the paper provides a systematic overview of state-of-the-art methods and proposes a three-phase problem categorization scheme. This review aims to facilitate the exploration of contemporary technical strategies, industrial applications, and future directions for addressing data change challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is getting smarter every day! But sometimes, unexpected changes in the data can make AI models work poorly or not at all. Two main areas of research try to solve this problem: domain shift and concept drift. These two fields are actually very similar, which means they use similar technical approaches. This paper brings these two fields together under one umbrella, called the “data change problem.” It looks at what’s being done already in both fields, proposes a new way to organize ideas, and suggests ways that researchers can explore this area further. |
