Summary of Multi-armed Bandit Approach For Optimizing Training on Synthetic Data, by Abdulrahman Kerim et al.
Multi-Armed Bandit Approach for Optimizing Training on Synthetic Data
by Abdulrahman Kerim, Leandro Soriano Marcolino, Erickson R. Nascimento, Richard Jiang
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel UCB-based training procedure combines with a dynamic usability metric to assess the usability of synthetically generated data. This approach integrates low-level and high-level information from synthetic images and their corresponding real and synthetic datasets, surpassing existing traditional metrics. The method adapts to changes in the machine learning model’s state and considers the evolving utility of training samples during the training process. The proposed attribute-aware bandit pipeline generates synthetic data by integrating a Large Language Model with Stable Diffusion. Quantitative results show that this approach can boost the performance of a wide range of supervised classifiers, achieving an improvement of up to 10% in classification accuracy compared to traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial data to train machine learning models. The authors want to know if this synthetic data is good enough for real-world use. They created a new way to measure how useful the synthetic data is and used it with an algorithm that adjusts its training process based on the usefulness of the data. They also developed a method to generate more realistic synthetic data using large language models. The results show that their approach can improve the performance of many types of machine learning models. |
Keywords
» Artificial intelligence » Classification » Diffusion » Large language model » Machine learning » Supervised » Synthetic data
