Summary of Data Augmentation with Variational Autoencoder For Imbalanced Dataset, by Samuel Stocksieker et al.
Data Augmentation with Variational Autoencoder for Imbalanced Dataset
by Samuel Stocksieker, Denys Pommeret, Arthur Charpentier
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces a novel method for learning from imbalanced regression (IR) tabular data, a significant challenge in predictive modeling. The proposed approach uses variational autoencoders (VAEs) to model complex distributions, but combines them with a smoothed bootstrap to efficiently address IR challenges. The method is numerically investigated through simulations and datasets known for IR, demonstrating its effectiveness compared to competitors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in learning from data: when the data isn’t balanced or fair. This can make it hard for computers to learn and make good predictions. The authors propose a new way to fix this using something called variational autoencoders (VAEs). They combine these with another tool, a smoothed bootstrap, to make sure they’re working well on imbalanced data. They tested their method on simulations and real datasets to see how it compares to other approaches. |
Keywords
» Artificial intelligence » Regression
