Summary of Dataset Growth, by Ziheng Qin et al.
Dataset Growth
by Ziheng Qin, Zhaopan Xu, Yukun Zhou, Zangwei Zheng, Zebang Cheng, Hao Tang, Lei Shang, Baigui Sun, Xiaojiang Peng, Radu Timofte, Hongxun Yao, Kai Wang, Yang You
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 paper proposes a novel algorithm called InfoGrowth to tackle the challenges of dealing with exponentially growing datasets in deep learning applications. The existing techniques for cleaning and selecting data are mainly designed for offline settings, which can lead to sub-optimal efficiency when handling large-scale datasets. InfoGrowth is an online algorithm that efficiently cleans and selects data while maintaining awareness of cleanliness and diversity, making it practical for real-world data engines. The algorithm demonstrates improved data quality and efficiency on both single-modal and multi-modal tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in using artificial intelligence. As more data becomes available, it’s getting harder to clean and organize this data efficiently. The authors propose a new way to do this called InfoGrowth, which can handle large amounts of data growing rapidly. This method improves the quality and efficiency of the data, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Deep learning » Multi modal
