Summary of Generalized Regression with Conditional Gans, by Deddy Jobson and Eddy Hudson
Generalized Regression with Conditional GANs
by Deddy Jobson, Eddy Hudson
First submitted to arxiv on: 21 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 approach to regression uses conditional generative adversarial networks to learn a prediction function that can generate feature-label pairs similar to those in the training dataset. This method makes fewer assumptions about the data distribution and has better representation capabilities compared to traditional regression methods. The approach is demonstrated to be superior on multiple synthetic and real-world datasets, particularly with heavy-tailed regression datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of doing regression is proposed by using conditional generative adversarial networks. Instead of trying to fit a prediction function to the data, this method tries to learn a function that can generate fake feature-label pairs that are hard to tell apart from real ones in the training dataset. This approach has better representation capabilities and makes fewer assumptions about the data distribution. It’s shown to work well on many datasets, especially those with heavy-tailed regression. |
Keywords
» Artificial intelligence » Regression
