Summary of Rankup: Boosting Semi-supervised Regression with An Auxiliary Ranking Classifier, by Pin-yen Huang et al.
RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier
by Pin-Yen Huang, Szu-Wei Fu, Yu Tsao
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 Medium Difficulty summary: State-of-the-art semi-supervised learning techniques, such as FixMatch and its variants, have achieved impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. The paper introduces RankUp, a simple yet effective approach that adapts existing semi-supervised classification techniques to enhance the performance of regression tasks by converting the original task into a ranking problem and training it concurrently with the original objective. RankUp achieves state-of-the-art results in a range of regression benchmarks across computer vision, audio, and natural language processing tasks. Additionally, the paper proposes regression distribution alignment (RDA) as a complementary technique to further enhance RankUp’s performance by refining pseudo-labels through distribution alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper solves a big problem in machine learning. Currently, there are powerful tools for classifying things into categories, but these don’t work well for predicting continuous values like temperatures or scores. The authors create a new method called RankUp that takes the classification tools and adapts them to work better for regression tasks. They also introduce a second technique called RDA that helps improve the results even more. In tests, RankUp achieves the best results so far in many different areas, including images, sounds, and natural language. |
Keywords
» Artificial intelligence » Alignment » Classification » Machine learning » Natural language processing » Regression » Semi supervised
